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Tools, techniques, datasets and application areas for object detection in an image: a review

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Abstract

Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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References

  1. Afif M, Ayachi R, Pissaloux E, Said Y, Atri M (2020) Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people. Multimed Tools Appl 79(41–42):31645–31662.https://doi.org/10.1007/s11042-020-09662-3

    Article  Google Scholar 

  2. Alam A, Jaffery ZA (2020) Indian Traffic Sign Detection and Recognition. Int J Intell Transp Syst Res 18(1):98–112.https://doi.org/10.1007/s13177-019-00178-1

    Article  Google Scholar 

  3. Bach M, Stumper D, Dietmayer K (2018) Deep Convolutional Traffic Light Recognition for Automated Driving, in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, vol. 2018-Novem, 851–858,https://doi.org/10.1109/ITSC.2018.8569522

  4. Banerjee K, Notz D, Windelen J, Gavarraju S, He M (2018) Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving, in 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, vol. 2018-June, no. Iv, 1632–1638,https://doi.org/10.1109/IVS.2018.8500699

  5. Becker BC, Ortiz EG (2008) Evaluation of face recognition techniques for application to facebook, in 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE, pp. 1–6,https://doi.org/10.1109/AFGR.2008.4813471

  6. Behrendt K, Novak L, Botros R (2017) A deep learning approach to traffic lights: Detection, tracking, and classification, in 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 1370–1377,https://doi.org/10.1109/ICRA.2017.7989163

  7. Bhandari A, Prasad PWC, Alsadoon A, Maag A (2021) Object detection and recognition: using deep learning to assist the visually impaired, Disabil Rehabil Assist Technol, 1–9, 2019, Taylor & Francis,https://doi.org/10.1080/17483107.2019.1673834

  8. Bhangale U, Patil S, Vishwanath V, Thakker P, Bansode A, Navandhar D (2020, Elsevier B.V.) Near real-time crowd counting using deep learning approach. Procedia Comput Sci 171(2019):770–779.https://doi.org/10.1016/j.procs.2020.04.084

    Article  Google Scholar 

  9. Bochkovskiy A, Wang C, Liao HM (2020) YOLOv4: Optimal Speed and Accuracy of Object Detection, [Online]. Available:http://arxiv.org/abs/2004.10934

  10. Bouras C, Michos E (2022) An online real-time face recognition system for police purposes, in 2022 International Conference on Information Networking (ICOIN), IEEE, pp. 62–67,https://doi.org/10.1109/ICOIN53446.2022.9687212

  11. Bouti A, Mahraz MA, Riffi J, Tairi H (2020, Springer Berlin Heidelberg) A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network. Soft Comput 24(9):6721–6733.https://doi.org/10.1007/s00500-019-04307-6

    Article  Google Scholar 

  12. Braun M, Krebs S, Flohr F, Gavrila DM (2019, IEEE) EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes. IEEE Trans Pattern Anal Mach Intell 41(8):1844–1861.https://doi.org/10.1109/TPAMI.2019.2897684

    Article  Google Scholar 

  13. Caesar H et al. (2020) nuScenes: A multimodal dataset for autonomous driving, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, no. March, pp 11618–11628,https://doi.org/10.1109/CVPR42600.2020.01164

  14. Ch’ng CK, Chan CS (2017) Total-Text: a comprehensive dataset for scene text detection and recognition, in2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 935–942,https://doi.org/10.1109/ICDAR.2017.157

  15. Chatterjee S, Zunjani FH, Nandi GC (2020) Real-time object detection and recognition on low-compute humanoid robots using deep learning, in 2020 6th International Conference on Control, Automation and Robotics (ICCAR), IEEE, pp. 202–208,https://doi.org/10.1109/ICCAR49639.2020.9108054.

  16. Chen IK, Chi CY, Hsu SL, Chen LG (2014) A real-time system for object detection and location reminding with RGB-D camera, 2014 IEEE Int.Conf Consum. Electron., 412–413,https://doi.org/10.1109/ICCE.2014.6776063

  17. Chen Z, Luo R, Li J, Du J, Wang C (2021, Taylor & Francis) U-Net based road area guidance for crosswalks detection from remote sensing images. Can J Remote Sens 47(1):83–99.https://doi.org/10.1080/07038992.2021.1894915

    Article  Google Scholar 

  18. Chen Y, Wang W, Zhou Y, Yang F, Yang D, Wang W (2021) Self-training for domain adaptive scene text detection, in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, pp. 850–857,https://doi.org/10.1109/ICPR48806.2021.9412558

  19. Chen Z, Ouyang W, Liu T, Tao D (2021, Springer US) A shape transformation-based dataset augmentation framework for pedestrian detection. Int J Comput Vis 129(4):1121–1138.https://doi.org/10.1007/s11263-020-01412-0

    Article  Google Scholar 

  20. Cheng G, Han J (2016) A survey on object detection in optical remote sensing images, ISPRS J Photogramm Remote Sens, 117, 11–28, Elsevier,https://doi.org/10.1016/j.isprsjprs.2016.03.014.

  21. Cordts M et al. (2016) The Cityscapes Dataset for Semantic Urban Scene Understanding, in 2016 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, 29(5), 3213–3223,https://doi.org/10.1109/CVPR.2016.350.

  22. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection, in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), IEEE, pp. 886–893,https://doi.org/10.1109/CVPR.2005.177

  23. Dam GC, Management A (2019) U. S. Geological survey grand canyon monitoring fiscal year 2019 Annual Project Report to the Glen Canyon Dam Adaptive Management

  24. Dasiopoulou S, Giannakidou E, Litos G, Malasioti P, Kompatsiaris Y (2011) A survey of semantic image and video annotation tools, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. LNAI 6050, Springer, Springer, pp. 196–239

  25. de Charette R, Nashashibi F (2009) Traffic light recognition using image processing compared to learning processes, in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 333–338,https://doi.org/10.1109/IROS.2009.5353941

  26. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition, IEEE, 248–255,https://doi.org/10.1109/CVPR.2009.5206848

  27. Dhivya S, Sangeetha J, Sudhakar B (2020, Springer Berlin Heidelberg) Copy-move forgery detection using SURF feature extraction and SVM supervised learning technique. Soft Comput 24(19):14429–14440.https://doi.org/10.1007/s00500-020-04795-x

    Article  Google Scholar 

  28. Dollar P, Wojek C, Schiele B, Perona P (2009) Pedestrian detection: A benchmark, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 304–311,https://doi.org/10.1109/CVPR.2009.5206631

  29. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art, in IEEE transactions on pattern analysis and machine intelligence 34(4), 743–761,https://doi.org/10.1109/TPAMI.2011.155

  30. Dominguez-Sanchez A, Orts-Escolano S, Garcia-Rodriguez J, Cazorla M (2018) A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection, in 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8,https://doi.org/10.1109/IJCNN.2018.8489478.

  31. Du F, Wang WL, Zhang Z (2020) Pedestrian detection based on a hybrid Gaussian model and support vector machine, Enterp Inf Syst, 1–12, Taylor & Francis,https://doi.org/10.1080/17517575.2020.1791363.

  32. Dutta A, Zisserman A (2019) The VIA Annotation Software for Images, Audio and Video, in Proceedings of the 27th ACM International Conference on Multimedia, ACM, pp. 2276–2279,https://doi.org/10.1145/3343031.3350535.

  33. Ertler C, Mislej J, Ollmann T, Porzi L, Neuhold G, Kuang Y (2019) The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale, Comput Vis Pattern Recognit, 1–17, [Online]. Available:http://arxiv.org/abs/1909.04422

  34. Everingham M et al. (2006) The 2005 PASCAL Visual Object Classes Challenge, in Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science, Springer Berlin Heidelberg, vol. 3944 LNAI, pp. 117–176,https://doi.org/10.1007/11736790_8.

  35. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338.https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  36. Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2015, Springer) The pascal visual object classes challenge: A Retrospective. Int J Comput Vis 111(1):98–136.https://doi.org/10.1007/s11263-014-0733-5

    Article  Google Scholar 

  37. Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model, in 2008 IEEE conference on computer vision and pattern recognition, IEEE, 1–8,https://doi.org/10.1109/CVPR.2008.4587597

  38. Fregin A, Muller J, Krebel U, Dietmayer K (2018) The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets, in 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 3376–3383,https://doi.org/10.1109/ICRA.2018.8460737

  39. Fu M, Huang Y (2010) A survey of traffic sign recognition, in 2010 International Conference on Wavelet Analysis and Pattern Recognition, IEEE, pp. 119–124,https://doi.org/10.1109/ICWAPR.2010.5576425

  40. Fu K, Chang Z, Zhang Y, Xu G, Zhang K, Sun X (2020, Elsevier) Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images. ISPRS J Photogramm Remote Sens 161(January):294–308.https://doi.org/10.1016/j.isprsjprs.2020.01.025

    Article  Google Scholar 

  41. Fu J, Zhao C, Xia Y, Liu W (2020) Vehicle and wheel detection: a novel SSD-based approach and associated large-scale benchmark dataset. Multimed Tools Appl 79(17–18):12615–12634.https://doi.org/10.1007/s11042-019-08523-y

    Article  Google Scholar 

  42. Fu C, Liu W, Ranga A, Tyagi A, Berg AC (n.d.) DSSD : Deconvolutional Single Shot Detector

  43. Gawande U, Hajari K, Golhar Y (2022) SIRA: scale illumination rotation affine invariant mask R-CNN for pedestrian detection. Appl Intell, no. 0123456789, Springer US,https://doi.org/10.1007/s10489-021-03073-z.

  44. Ge Z, Wang J, Huang X, Liu S, Yoshie O (2021, Elsevier) LLA: loss-aware label assignment for dense pedestrian detection. Neurocomputing 462:272–281.https://doi.org/10.1016/j.neucom.2021.07.094

    Article  Google Scholar 

  45. Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite”, in 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp. 3354–3361,https://doi.org/10.1109/CVPR.2012.6248074

  46. Girshick R (2015) Fast R-CNN, in 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, pp. 1440–1448,https://doi.org/10.1109/ICCV.2015.169

  47. Girshick R, Donahue J, Darrell T, Malik J (2015) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158.https://doi.org/10.1109/TPAMI.2015.2437384

    Article  Google Scholar 

  48. Godinho De Oliveira BA, Ferreira FMF, Martins CAPDS (2018) Fast and lightweight object detection network: detection and recognition on resource constrained devices. IEEE Access 101(1):8714–8724.https://doi.org/10.1109/ACCESS.2018.2801813

    Article  Google Scholar 

  49. Grosicki E, El-Abed H (2011) ICDAR 2011 - French Handwriting Recognition Competition, in 2011 International Conference on Document Analysis and Recognition, IEEE, pp. 1459–1463,https://doi.org/10.1109/ICDAR.2011.290

  50. Guo Z, Liao W, Xiao Y, Veelaert P, Philips W (2021, Elsevier) Weak segmentation supervised deep neural networks for pedestrian detection. Pattern Recognit 119:108063.https://doi.org/10.1016/j.patcog.2021.108063

    Article  Google Scholar 

  51. Gupta S, Thakur K, Kumar M (2021, Springer) 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. Vis Comput 37(3):447–456.https://doi.org/10.1007/s00371-020-01814-8

    Article  Google Scholar 

  52. Hadid A, Heikkila JY, Silven O, Pietikainen M (2007) Face and Eye Detection for Person Authentication in Mobile Phones, in 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, IEEE, pp. 101–108,https://doi.org/10.1109/ICDSC.2007.4357512

  53. Halaschek-Wiener C, Golbeck J, Schain A, Grove M, Parsia B, Hendler J (2005) Photostuff - an image annotation tool for the semantic web, 4th Int. Semant. Web Conf. Poster Pap., pp. 2–4

  54. Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100.https://doi.org/10.1109/MSP.2017.2749125

    Article  Google Scholar 

  55. Han C, Gao G, Zhang Y (2019) Real-time small traffic sign detection with revised faster-RCNN. Multimed Tools Appl 78(10):13263–13278.https://doi.org/10.1007/s11042-018-6428-0

    Article  Google Scholar 

  56. Harzallah H, Jurie F, Schmid C (2009) Combining efficient object localization and image classification, in 2009 IEEE 12th International Conference on Computer Vision, IEEE, pp. 237–244,https://doi.org/10.1109/ICCV.2009.5459257

  57. He K, Zhang X, Ren S, Sun J (Sep. 2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916.https://doi.org/10.1109/TPAMI.2015.2389824

    Article  Google Scholar 

  58. He W, Zhang X-Y, Yin F, Luo Z, Ogier J-M, Liu C-L (2020, Elsevier Ltd) Realtime multi-scale scene text detection with scale-based region proposal network. Pattern Recognit 98:1–14.https://doi.org/10.1016/j.patcog.2019.107026

    Article  Google Scholar 

  59. He K, Gkioxari G, Dollar P, Girshick R (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 42(2):386–397, IEEE.https://doi.org/10.1109/TPAMI.2018.284

    Article  Google Scholar 

  60. Heitz G, Koller D (2008) Learning spatial context: using stuff to find things, in European conference on computer vision, Springer, Berlin, Heidelberg, 30–43,https://doi.org/10.1007/978-3-540-88682-2_4.

  61. Hosni Mahmoud HA, Mengash HA (2021, springer) A novel technique for automated concealed face detection in surveillance videos. Pers Ubiquitous Comput 25(1):129–140.https://doi.org/10.1007/s00779-020-01419-x

    Article  Google Scholar 

  62. Houben S, Stallkamp J, Salmen J, Schlipsing M, Igel C (2013) Detection of traffic signs in real-world images: The German traffic sign detection benchmark, in The 2013 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8,https://doi.org/10.1109/IJCNN.2013.6706807

  63. Hu J, Zhao Y, Zhang X (2020) Application of transfer learning in infrared pedestrian detection, in 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), IEEE, pp. 1–4,https://doi.org/10.1109/ICIVC50857.2020.9177438

  64. Hua X, Wang X, Rui T, Zhang H, Wang D (2020, Elsevier B.V.) A fast self-attention cascaded network for object detection in large scene remote sensing images. Appl Soft Comput 94:106495.https://doi.org/10.1016/j.asoc.2020.106495

    Article  Google Scholar 

  65. Huang Z et al. (2019) ICDAR2019 competition on scanned receipt OCR and information extraction, Proc Int Conf Doc Anal. Recognition, ICDAR, pp. 1516–1520,https://doi.org/10.1109/ICDAR.2019.00244.

  66. Huang Q, Cai Z, Lan T (2021, IEEE) A single neural network for mixed style license plate detection and recognition. IEEE Access 9:21777–21785.https://doi.org/10.1109/ACCESS.2021.3055243

    Article  Google Scholar 

  67. Hung BT (2021) Face recognition using hybrid HOG-CNN approach, in International Journal of Image and Graphics, 1254, 715–723

  68. Hung GL, Bin Sahimi MS, Samma H, Almohamad TA, Lahasan B (2020, Springer) Faster R-CNN deep learning model for pedestrian detection from drone images. SN Comput Sci 1(2):116.https://doi.org/10.1007/s42979-020-00125-y

    Article  Google Scholar 

  69. Irbaz MS, Al Nasim MA, Ferdous RE (2022) Real-time face recognition system for remote employee tracking. Lecture Notes on Data Engineering and Communications Technologies 95:153–163

    Article  Google Scholar 

  70. Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2014) Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition, pp. 1–10, [Online]. Available:http://arxiv.org/abs/1406.2227

  71. Jakob J, Tick J (2020) Camera-based on-road detections for the visually impaired. Acta Polytech Hungarica 17(3):125–146.https://doi.org/10.12700/APH.17.3.2020.3.7

    Article  Google Scholar 

  72. Jamtsho Y, Riyamongkol P, Waranusast R (2021, Elsevier B.V.) Real-time license plate detection for non-helmeted motorcyclist using YOLO. ICT Express 7(1):104–109.https://doi.org/10.1016/j.icte.2020.07.008

    Article  Google Scholar 

  73. Jin Y, Zhang Y, Cen Y, Li Y, Mladenovic V, Voronin V (2021, Elsevier Ltd) Pedestrian detection with super-resolution reconstruction for low-quality image. Pattern Recognit 115:107846.https://doi.org/10.1016/j.patcog.2021.107846

    Article  Google Scholar 

  74. Karatzas D, Mestre SR, Mas J, Nourbakhsh F, Roy PP (2011) ICDAR 2011 Robust Reading Competition - Challenge 1: Reading Text in Born-Digital Images (Web and Email), in 2011 International Conference on Document Analysis and Recognition, IEEE, pp. 1485–1490,https://doi.org/10.1109/ICDAR.2011.295.

  75. Kaur RP, Kumar M, Jindal MK (2022) Performance evaluation of different features and classifiers for Gurumukhi newspaper text recognition. J Ambient Intell Humaniz Comput no. 0123456789, Springer,https://doi.org/10.1007/s12652-021-03687-8

  76. Khurana K, Awasthi R (2013) Techniques for object recognition in images and multi-object detection. Int J Adv Res Comput Eng Technol 2(4):1383–1388

    Google Scholar 

  77. Klare BF et al. (2015) Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A, in 2015 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp. 1931–1939,https://doi.org/10.1109/CVPR.2015.7298803.

  78. Kostinger M, Wohlhart P, Roth PM, Bischof H (2011) Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization, in 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, pp. 2144–2151,https://doi.org/10.1109/ICCVW.2011.6130513

  79. Kumar R, Kumar S, Chand P, Lal S (2014) Object detection and recognition for a pick and place robot, in IEEE Asia-Pacific world congress on computer science and Engineering, 2014, 2–9,https://doi.org/10.13140/2.1.4379.2165

  80. Kumar A, Kumar M, Kaur A (2021, Springer) Face detection in still images under occlusion and non-uniform illumination. Multimed Tools Appl 80(10):14565–14590.https://doi.org/10.1007/s11042-020-10457-9

    Article  Google Scholar 

  81. Kuznetsova A, Maleva T, Soloviev V (2020) Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images, in Neurocomputing, 149, no. Part A, 233–243

  82. Kuznetsova A et al (2020, Springer) The open images dataset V4. Int J Comput Vis 128(7):1956–1981.https://doi.org/10.1007/s11263-020-01316-z

    Article  Google Scholar 

  83. LabelBox (2018)https://github.com/Labelbox/Labelbox/blob/master/README.md.

  84. Lam D et al. (2018) xView: Objects in Context in Overhead Imagery, [Online]. Available:http://arxiv.org/abs/1802.07856

  85. Lamba PS, Virmani D, Castillo O (2020, Springer Berlin Heidelberg) Multimodal human eye blink recognition method using feature level fusion for exigency detection. Soft Comput 24(22):16829–16845.https://doi.org/10.1007/s00500-020-04979-5

    Article  Google Scholar 

  86. Laroca R, Zanlorensi LA, Gonçalves GR, Todt E, Schwartz WR, Menotti D (2021, wiley) An efficient and layout-independent automatic license plate recognition system based on the YOLO detector. IET Intell Transp Syst 15(4):1–21.https://doi.org/10.1049/itr2.12030

    Article  Google Scholar 

  87. Learned-Miller E, Jain V (2010) FDDB : a benchmark for face detection in unconstrained settings

  88. Li J, Liang X, Wei Y, Xu T, Feng J, Yan S (2017) Perceptual generative adversarial networks for small object detection, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, 1951–1959,https://doi.org/10.1109/CVPR.2017.211

  89. Li K, Wan G, Cheng G, Meng L, Han J (2020, Elsevier) Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J Photogramm Remote Sens 159(2019):296–307.https://doi.org/10.1016/j.isprsjprs.2019.11.023

    Article  Google Scholar 

  90. Li F, Luo Z, Huang J, Wang L, Cai J, Huang Y (2020) AlTwo: Vehicle Recognition in foggy weather based on two-step recognition algorithm, in Neurocomputing 149, no. Part A, Springer, Springer, pp. 130–141.

  91. Li C et al (2020, Elsevier B.V.) A parallel down-up fusion network for salient object detection in optical remote sensing images. Neurocomputing 415:411–420.https://doi.org/10.1016/j.neucom.2020.05.108

    Article  Google Scholar 

  92. Liao J, Liu Y, Piao Y, Su J, Cai G, Wu Y (2022, Springer) GLE-Net: A global and local ensemble network for aerial object detection. Int J Comput Intell Syst 15(1):2.https://doi.org/10.1007/s44196-021-00056-3

    Article  Google Scholar 

  93. Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection, in 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2999–3007,https://doi.org/10.1109/ICCV.2017.324.

  94. Liu K, Mattyus G (2015) Fast multiclass vehicle detection on aerial images. IEEE Geosci Remote Sens Lett 12(9):1938–1942.https://doi.org/10.1109/LGRS.2015.2439517

    Article  Google Scholar 

  95. Liu W et al. (2016) SSD: Single Shot MultiBox Detector, in European conference on computer vision, Springer, Springer, 21–37

  96. Liu Z, Wang H, Weng L, Yang Y (2016) Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds, IEEE Geosci Remote Sens Lett vol. 13, no. 8, pp. 1074–1078, IEEE,https://doi.org/10.1109/LGRS.2016.2565705

  97. Liu D, Cui Y, Chen Y, Zhang J, Fan B (2020, Elsevier B.V.) Video object detection for autonomous driving: motion-aid feature calibration. Neurocomputing 409:1–11.https://doi.org/10.1016/j.neucom.2020.05.027

    Article  Google Scholar 

  98. Liu L et al (2020, Springer US) Deep Learning for Generic Object Detection: A Survey. Int J Comput Vis 128(2):261–318.https://doi.org/10.1007/s11263-019-01247-4

    Article MATH  Google Scholar 

  99. Liu Y, Liu J, Ning X, Li J (2022, Taylor & Francis) MS-CNN: multiscale recognition of building rooftops from high spatial resolution remote sensing imagery. Int J Remote Sens 43(1):270–298.https://doi.org/10.1080/01431161.2021.2018146

    Article  Google Scholar 

  100. Lu Y, Lu J, Zhang S, Hall P (2018, Springer) Traffic signal detection and classification in street views using an attention model. Comput Vis Media 4(3):253–266.https://doi.org/10.1007/s41095-018-0116-x

    Article  Google Scholar 

  101. Lu W, Zhou Y, Wan G, Hou S, Song S (2019) L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, vol. 2019-June, 6382–6391,https://doi.org/10.1109/CVPR.2019.00655

  102. Lu X, Ji J, Xing Z, Miao Q (2021) Attention and feature fusion SSD for remote sensing object detection. IEEE Trans Instrum Meas 70,https://doi.org/10.1109/TIM.2021.3052575

  103. Lucas SM (2005) ICDAR 2005 text locating competition results, in Eighth International Conference on Document Analysis and Recognition (ICDAR’05), IEEE, vol. 2005, pp. 80–84 Vol. 1,https://doi.org/10.1109/ICDAR.2005.231.

  104. Lucas SM, Panaretos A, Sosa L, Tang A, Wong S, Young R (2003) ICDAR 2003 robust reading competitions, in Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., IEEE, vol. 1, 682–687,https://doi.org/10.1109/ICDAR.2003.1227749

  105. Lux M (2009) Caliph & Emir: MPEG-7 photo annotation and retrieval, MM’09 - Proc. 2009 ACM Multimed. Conf. with Co-located Work. Symp 925–926,https://doi.org/10.1145/1631272.1631456

  106. Lv X, Su M, Wang Z (2021) Application of face recognition method under deep learning algorithm in embedded systems. Microprocess. Microsyst, 104034, Elsevier B.V.,https://doi.org/10.1016/j.micpro.2021.104034

  107. Ma C, Sun L, Zhong Z, Huo Q (2021) ReLaText: exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks. Pattern Recogn 111:107684.https://doi.org/10.1016/j.patcog.2020.107684

    Article  Google Scholar 

  108. Madani M, Bagheri M, Sahba R, Sahba A (2011) Real time object detection using a novel adaptive color thresholding method, MM’11 - Proc. 2011 ACM Multimed. Conf. Co-Located Work. - Ubi-MUI 2011 Work. Ubi-MUI’11, pp. 13–16,https://doi.org/10.1145/2072652.2072656

  109. Maeda H, Kashiyama T, Sekimoto Y, Seto T, Omata H (2021, wiley) Generative adversarial network for road damage detection. Comput Civ Infrastruct Eng 36(1):1–14.https://doi.org/10.1111/mice.12561

    Article  Google Scholar 

  110. Manikandan NS, Ganesan K (2019) Deep learning based automatic video annotation tool for self-driving car, [Online]. Available:http://arxiv.org/abs/1904.12618

  111. Masita KL, Hasan AN, Shongwe T (2022) Refining the efficiency of R-CNN in Pedestrian Detection. Lecture Notes in Networks and Systems 216:1–14

    Article  Google Scholar 

  112. Mathias M, Timofte R, Benenson R, Van Gool L (2013) Traffic sign recognition - how far are we from the solution?, Proc Int Jt Conf Neural Networks,https://doi.org/10.1109/IJCNN.2013.6707049

  113. Maze B et al. (2018) IARPA Janus Benchmark - C: Face Dataset and Protocol, in 2018 International Conference on Biometrics (ICB), IEEE, pp. 158–165,https://doi.org/10.1109/ICB2018.2018.00033

  114. Mehedi Shamrat FMJ, Al Jubair M, Billah MM, Chakraborty S, Alauddin M, Ranjan R (2021) A Deep Learning Approach for Face Detection using Max Pooling, in 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, no. June, pp 760–764,https://doi.org/10.1109/ICOEI51242.2021.9452896

  115. Mehta R, Ozturk C (2019) Object Detection at 200 Frames per Second, in Lecture Notes in Computer Science, 11133 LNCS, Springer, Springer, 659–675

  116. Mei X, Hong Z, Prokhorov D, Tao D (2015, IEEE) Robust multitask multiview tracking in videos. IEEE Trans Neural Networks Learn Syst 26(11):2874–2890.https://doi.org/10.1109/TNNLS.2015.2399233

    Article MathSciNet  Google Scholar 

  117. Melnyk P, You Z, Li K (2020, Springer Berlin Heidelberg) A high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Comput 24(11):7977–7987.https://doi.org/10.1007/s00500-019-04083-3

    Article  Google Scholar 

  118. Merkulova IY, Shavetov SV, Borisov OI, Gromov VS (2019, Elsevier Ltd) Object detection and tracking basics: Student education. IFAC-PapersOnLine 52(9):79–84.https://doi.org/10.1016/j.ifacol.2019.08.128

    Article  Google Scholar 

  119. Mishra A, Alahari K, Jawahar C (2012) Scene Text Recognition using Higher Order Language Priors, in Procedings of the British Machine Vision Conference 2012, British Machine Vision Association, pp. 127.1–127.11,https://doi.org/10.5244/C.26.127

  120. Mogelmose A, Trivedi MM, Moeslund TB (2012) Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Transp Syst 13(4):1484–1497.https://doi.org/10.1109/TITS.2012.2209421

    Article  Google Scholar 

  121. Murdock M, Reid S, Hamilton B, Reese J (2015) ICDAR 2015 competition on text line detection in historical documents, in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1171–1175,https://doi.org/10.1109/ICDAR.2015.7333945

  122. Nada H, Sindagi VA, Zhang H, Patel VM (2018) Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results, in 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), IEEE, pp. 1–10,https://doi.org/10.1109/BTAS.2018.8698561

  123. Naiemi F, Ghods V, Khalesi H (2021, Elsevier Ltd) A novel pipeline framework for multi oriented scene text image detection and recognition. Expert Syst Appl 170(2020):114549.https://doi.org/10.1016/j.eswa.2020.114549

    Article  Google Scholar 

  124. Nayagam M, Ramar K (2015) A survey on real time object detection and tracking algorithms. International Journal of Applied Engineering Research 10(9):8290–8297

    Google Scholar 

  125. Nepal U, Eslamiat H (2022) Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs. Sensors 22(2):464.https://doi.org/10.3390/s22020464

    Article  Google Scholar 

  126. Neumann L et al. (2019) NightOwls: A Pedestrians at Night Dataset”, in Computer Vision – ACCV 2018, vol. 11361, H. Li, G. Mori, and K. Schindler, Eds. Springer International Publishing, Springer International Publishing, pp. 691–705

  127. Nguyen CC, Tran GS, Nghiem TP, Burie J-C, Luong CM (2019) Real-time smile detection using deep learning. J Comput Sci Cybern 35(2):135–145.https://doi.org/10.15625/1813-9663/35/2/13315

    Article  Google Scholar 

  128. Nguyen N-D, Do T, Ngo TD, Le D-D (2020) An evaluation of deep learning methods for small object detection. J Electr Comput Eng 2020:1–18.https://doi.org/10.1155/2020/3189691

    Article  Google Scholar 

  129. Ogura R, Nagasaki T, Matsubara H (2020) Improving the visibility of nighttime images for pedestrian recognition using in-vehicle camera. Electron Commun Japan 103(10):35–43.https://doi.org/10.1002/ecj.12268

    Article  Google Scholar 

  130. Padilla R, Netto SL, da Silva EABB (2020) A Survey on Performance Metrics for Object-Detection Algorithms”, in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE, vol. 2020-July, 237–242,https://doi.org/10.1109/IWSSIP48289.2020.9145130

  131. Papageorgiou C, Poggio T (2000, Springer) Trainable system for object detection. Int J Comput Vis 38(1):15–33.https://doi.org/10.1023/A:1008162616689

    Article MATH  Google Scholar 

  132. Pattewar T, Chaudhari A, Marathe M, Bhol M (2019) Real-time object detection : a survey. Int Res J Eng Technol 06(04):231–237

    Google Scholar 

  133. Paul V, Michael J (2001) Robust real-time object detection. Int J Comput Vis 57:1–25

    Google Scholar 

  134. Qian R, Lai X, Li X (2021) 3D object detection for autonomous driving: A Survey 14(8), 1–24, [Online]. Available:http://arxiv.org/abs/2106.10823

  135. Qin S, Liu S (2021) Towards end-to-end car license plate location and recognition in unconstrained scenarios. Neural Comput Appl, pp. 1–11, Springer,https://doi.org/10.1007/s00521-021-06147-8

  136. Rahman MM, Al Mamun S, Kaiser MS, Islam MS, Rahman MA (2021) Cascade classification of face liveliness detection using heart beat measurement, in Advances in Intelligent Systems and Computing, vol. 1309, Springer, Springer, pp. 581–590

  137. Ravishankar V, Vinod V, Kumar T, Bhalla K (2022) Sensor integration and facial recognition deployment in a smart home system, Springer, Springer, pp. 759–771

  138. Razakarivony S, Jurie F (2016) Vehicle detection in aerial imagery : a small target detection benchmark. J Vis Commun Image Represent 34, 187–203, Elsevier,https://doi.org/10.1016/j.jvcir.2015.11.002

  139. Redmon J, Farhadi A (2017) YOLO9000: Better, Faster, Stronger, in 2017 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp. 6517–6525,https://doi.org/10.1109/CVPR.2017.690.

  140. Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement, Comput Vis Pattern Recognit, 1–6, arXiv preprint arXiv:1804.02767, [Online]. Available:http://arxiv.org/abs/1804.02767

  141. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, Real-Time Object Detection, in 2016 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, 779–788,https://doi.org/10.1109/CVPR.2016.91

  142. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149.https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  143. Risnumawan A, Shivakumara P, Chan CS, Tan CL (2014) A robust arbitrary text detection system for natural scene images. Expert Syst Appl 41(18):8027–8048, Elsevier.https://doi.org/10.1016/j.eswa.2014.07.008

    Article  Google Scholar 

  144. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252.https://doi.org/10.1007/s11263-015-0816-y

    Article MathSciNet  Google Scholar 

  145. Russell BC, Torralba A, Murphy KP, Freeman WT (2008) LabelMe: a database and web-based tool for image annotation. Int J Comput Vis 77(1–3):157–173.https://doi.org/10.1007/s11263-007-0090-8

    Article  Google Scholar 

  146. S SJ, P ER (2021, Elsevier GmbH) LittleYOLO-SPP: A delicate real-time vehicle detection algorithm. Optik (Stuttg) 225:165818.https://doi.org/10.1016/j.ijleo.2020.165818

    Article  Google Scholar 

  147. Saathoff C, Schenk S, Scherb A (2008) KAT : the K-space annotation tool. Proccedings SAMT, 1–2

  148. Sai Srinath NGS, Joseph AZ, Umamaheswaran S, Priyanka CL, Malavika Nair M, Sankaran P (2020, Elsevier BV) NITCAD - Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads. Procedia Comput Sci 171(2019):207–216.https://doi.org/10.1016/j.procs.2020.04.022

    Article  Google Scholar 

  149. Sanchez JA, Toselli AH, Romero V, Vidal E (2015) ICDAR 2015 competition HTRtS: Handwritten Text Recognition on the tranScriptorium dataset, in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1166–1170,https://doi.org/10.1109/ICDAR.2015.7333944.

  150. Sanchez JA, Romero V, Toselli AH, Villegas M, Vidal E (2017) ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset, in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1383–1388,https://doi.org/10.1109/ICDAR.2017.226.

  151. Santra S, Roy S, Sardar P, Deyasi A (2019) Real-time vehicle detection from captured images, 2019 Int. Conf. Opto-electronics. Appl Opt Optronix 2019, 1–4, IEEE,https://doi.org/10.1109/OPTRONIX.2019.8862323

  152. Schöller FET, Plenge-Feidenhans’L MK, Stets JD, Blanke M (2019) Assessing deep-learning methods for object detection at sea from LWIR images, in IFAC-PapersOnLine, Elsevier Ltd, 52(21), 64–71,https://doi.org/10.1016/j.ifacol.2019.12.284

  153. Setta S, Sinha S, Mishra M, Choudhury P (2022) Real-time facial recognition using SURF-FAST. Lecture Notes on Data Engineering and Communications Technologies 71:505–522

    Article  Google Scholar 

  154. Shahab A, Shafait F, Dengel A (2011) ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images, in 2011 International Conference on Document Analysis and Recognition, IEEE, pp. 1491–1496,https://doi.org/10.1109/ICDAR.2011.296

  155. Shao S et al. (2019) Objects365: A Large-Scale, High-Quality Dataset for Object Detection, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, pp. 8429–8438,https://doi.org/10.1109/ICCV.2019.00852

  156. Shao Z, Cheng G, Ma J, Wang Z, Wang J, Li D (2021) Real-time and accurate UAV pedestrian detection for social distancing monitoring in COVID-19 pandemic. IEEE Trans Multimed, pp. 1–1,https://doi.org/10.1109/TMM.2021.3075566.

  157. Sharma N, Mandal R, Sharma R, Pal U, Blumenstein M (2015) ICDAR2015 Competition on Video Script Identification (CVSI 2015), in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1196–1200,https://doi.org/10.1109/ICDAR.2015.7333950

  158. Shashirangana J et al (2021, wiley) License plate recognition using neural architecture search for edge devices. Int J Intell Syst:1–38.https://doi.org/10.1002/int.22471

  159. Shi Y, Zhang Z, Huang K, Ma W, Tu S (2020, Elsevier Inc) Human-computer interaction based on face feature localization. J vis Commun Image represent 70:1–6.https://doi.org/10.1016/j.jvcir.2019.102740

    Article  Google Scholar 

  160. Song X et al. (2019) APOLLOCAR3D: a large 3D car instance understanding benchmark for autonomous driving, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit vol. 2019-June, pp. 5447–5457, IEEE,https://doi.org/10.1109/CVPR.2019.00560

  161. Sudha D, Priyadarshini J (2020, Springer Berlin Heidelberg) An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm. Soft Comput 24(22):17417–17429.https://doi.org/10.1007/s00500-020-05042-z

    Article  Google Scholar 

  162. Sun Y et al. (2019) ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT, in 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1557–1562,https://doi.org/10.1109/ICDAR.2019.00250

  163. Sun P, Zheng Y, Zhou Z, Xu W, Ren Q (2020, Elsevier B.V) R4 Det: refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images. Image Vis Comput 103:1–26.https://doi.org/10.1016/j.imavis.2020.104036

    Article  Google Scholar 

  164. Sun F, Li H, Liu Z, Li X, Wu Z (2021, Taylor & Francis) Arbitrary-angle bounding box based location for object detection in remote sensing image. Eur J Remote Sens 54(1):102–116.https://doi.org/10.1080/22797254.2021.1880975

    Article  Google Scholar 

  165. Sun X, Wang P, Wang C, Liu Y, Fu K (2021, Elsevier) PBNet: part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS J Photogramm Remote Sens 173:50–65.https://doi.org/10.1016/j.isprsjprs.2020.12.015

    Article  Google Scholar 

  166. Susanto ER, Analia R, Sutopo PD, Soebakti H (2017) The deep learning development for real-time ball and goal detection of barelang-FC, in 2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), IEEE, pp. 146–151,https://doi.org/10.1109/ELECSYM.2017.8240393.

  167. Suzuki T, Kageyama Y, Ishizawa C (2020, wiley) Recognition method for speed limit signs and its applicability in recognition of vehicle entry prohibition signs at night. IEEJ Trans Electr Electron Eng 15(10):1–9.https://doi.org/10.1002/tee.23215

    Article  Google Scholar 

  168. Tamilselvi M, Karthikeyan S (2022, Elsevier) An ingenious face recognition system based on HRPSM_CNN under unrestrained environmental condition. Alexandria Eng J 61(6):4307–4321.https://doi.org/10.1016/j.aej.2021.09.043

    Article  Google Scholar 

  169. Tanner F et al. (2009) Overhead imagery research data set — an annotated data library & tools to aid in the development of computer vision algorithms, in 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), IEEE, 1–8,https://doi.org/10.1109/AIPR.2009.5466304

  170. Tarchoun B, Jegham I, Ben Khalifa A, Alouani I, Mahjoub MA (2020) Deep CNN-based Pedestrian Detection for Intelligent Infrastructure, in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), IEEE, pp. 1–6,https://doi.org/10.1109/ATSIP49331.2020.9231712

  171. Tian Z, Zhan R, Wang W, He Z, Zhang J, Zhuang Z (2020, Taylor & Francis) Object detection in optical remote sensing images by integrating object-to-object relationships. Remote Sens Lett 11(5):416–425.https://doi.org/10.1080/2150704X.2020.1722330

    Article  Google Scholar 

  172. Timofte R, Zimmermann K, Van Gool L (2014) Multi-view traffic sign detection, recognition, and 3D localisation, in Machine Vision and Applications, Springer, 25(3), 633–647,https://doi.org/10.1007/s00138-011-0391-3

  173. Tousch A-M, Herbin S, Audibert J-Y (2012) Semantic hierarchies for image annotation: A survey, in pattern recognition, Elsevier, 45(1), 333–345,https://doi.org/10.1016/j.patcog.2011.05.017

  174. Tran P, Pattichis M, Celedón-Pattichis S, LópezLeiva C (2021) Facial recognition in collaborative learning videos, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13053, no. 1613637, Springer, Springer, pp. 252–261

  175. Tzutalin (2015) Labelimg,https://github.com/tzutalin/label.

  176. Umer S, Rout RK, Pero C, Nappi M (2022, Springer) Facial expression recognition with trade-offs between data augmentation and deep learning features. J Ambient Intell Humaniz Comput 13(2):721–735.https://doi.org/10.1007/s12652-020-02845-8

    Article  Google Scholar 

  177. Varma S, Sreeraj M (2013) Object detection and classification in surveillance system, in 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 299–303,https://doi.org/10.1109/RAICS.2013.6745491

  178. Veit A, Matera T, Neumann L, Matas J, Belongie S (2016) COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images, [Online]. Available:http://arxiv.org/abs/1601.07140.

  179. Vennelakanti A, Shreya S, Rajendran R, Sarkar D, Muddegowda D, Hanagal P (2019) Traffic Sign Detection and Recognition using a CNN Ensemble, in 2019 IEEE International Conference on Consumer Electronics (ICCE), IEEE, pp. 1–4,https://doi.org/10.1109/ICCE.2019.8662019

  180. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, IEEE Comput. Soc, pp I-511-I–518,https://doi.org/10.1109/CVPR.2001.990517

  181. Viola P, Jones MJ (2003, Springer) Robust real-time face detection. Int J Comput Vis 57(2):137–154.https://doi.org/10.1023/B:VISI.0000013087.49260.fb

    Article  Google Scholar 

  182. VoTT: Vott (visual object tagging tool) (2019)https://github.com/microsoft/VoTT/blob/master/README.md.

  183. Wang K, Belongie S (2010) Word Spotting in the Wild, in 11th European Conference on Computer Vision, Springer, Springer, 591–604

  184. Wang H, Miao F (2022, Taylor & Francis) Building extraction from remote sensing images using deep residual U-Net. Eur J Remote Sens 55(1):71–85.https://doi.org/10.1080/22797254.2021.2018944

    Article  Google Scholar 

  185. Wang W, Shen J, Yang R, Porikli F (2018, IEEE) A unified spatiotemporal prior based on geodesic distance for video object segmentation. IEEE Trans Pattern Anal Mach Intell 40(1):20–33.https://doi.org/10.1109/TPAMI.2017.2662005

    Article  Google Scholar 

  186. Wang J, Jiang S, Song W, Yang Y (2019) A Comparative Study of Small Object Detection Algorithms, in 2019 Chinese Control Conference (CCC), IEEE, vol. 2019-July, pp. 8507–8512,https://doi.org/10.23919/ChiCC.2019.8865157

  187. Wang Y, Xie H, Zha Z, Xing M, Fu Z, Zhang Y (2020) ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 11753–11762,https://doi.org/10.1109/CVPR42600.2020.01177

  188. Wang G, Zhuang Y, Chen H, Liu X, Zhang T, Li L, Dong S, Sang Q (2022) FSoD-net: full-scale object detection from optical remote sensing imagery. IEEE Trans Geosci Remote Sens 60(c):1–18.https://doi.org/10.1109/TGRS.2021.3064599

    Article  Google Scholar 

  189. Wei X, Zhang H, Liu S, Lu Y (2020, Elsevier Ltd) Pedestrian detection in underground mines via parallel feature transfer network. Pattern Recognit 103:107195.https://doi.org/10.1016/j.patcog.2020.107195

    Article  Google Scholar 

  190. Womg A, Shafiee MJ, Li F, Chwyl B (2018) Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection, in 2018 15th conference on computer and robot vision (CRV), IEEE, 95–101,https://doi.org/10.1109/CRV.2018.00023.

  191. Wu S, Zhang L (2018) Using popular object detection methods for real time forest fire detection, in 2018 11th International Symposium on Computational Intelligence and Design (ISCID), IEEE, pp. 280–284,https://doi.org/10.1109/ISCID.2018.00070

  192. Wu X, Sahoo D, Hoi SCH (2020, Elsevier B.V.) Recent advances in deep learning for object detection. Neurocomputing 396:39–64.https://doi.org/10.1016/j.neucom.2020.01.085

    Article  Google Scholar 

  193. Wu J, Zhou C, Zhang Q, Yang M, Yuan J (2020) Self-mimic learning for small-scale pedestrian detection, in Proceedings of the 28th ACM International Conference on Multimedia, ACM, pp. 1–9,https://doi.org/10.1145/3394171.3413634

  194. Wu K, Bai C, Wang D, Liu Z, Huang T, Zheng H (2021, IEEE) Improved object detection algorithm of YOLOv3 remote sensing image. IEEE Access 9:113889–113900.https://doi.org/10.1109/ACCESS.2021.3103522

    Article  Google Scholar 

  195. Wu J et al (2022, Elsevier) A multimodal attention fusion network with a dynamic vocabulary for TextVQA. Pattern Recognit 122(108214):1–10.https://doi.org/10.1016/j.patcog.2021.108214

    Article  Google Scholar 

  196. Xia GS et al. (2018) DOTA: a large-scale dataset for object detection in aerial images, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 3974–3983,https://doi.org/10.1109/CVPR.2018.00418

  197. Xiao Y et al (2020) A review of object detection based on deep learning. Multimed. Tools Appl. 79(33–34):23729–23791.https://doi.org/10.1007/s11042-020-08976-6

    Article  Google Scholar 

  198. Xu H, Guo M, Nedjah N, Zhang J, Li P (2022) Vehicle and pedestrian detection algorithm based on lightweight YOLOv3-promote and semi-precision acceleration. IEEE Trans Intell Transp Syst, 1–12,https://doi.org/10.1109/TITS.2021.3137253

  199. Xu B et al (2022, Elsevier) CattleFaceNet: a cattle face identification approach based on RetinaFace and ArcFace loss. Comput. Electron Agric. 193:106675.https://doi.org/10.1016/j.compag.2021.106675

    Article  Google Scholar 

  200. Xue C, Lu S, Hoi S (2022, Elsevier) Detection and rectification of arbitrary shaped scene texts by using text keypoints and links. Pattern Recognit 124:1–31.https://doi.org/10.1016/j.patcog.2021.108494

    Article  Google Scholar 

  201. Yang B, Yan J, Lei Z, Li SZ (2015) Fine-grained evaluation on face detection in the wild, in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), IEEE, 1–7,https://doi.org/10.1109/FG.2015.7163158

  202. Yang S, Luo P, Loy CC, Tang X (2016) WIDER FACE: A Face Detection Benchmark, in 2016 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, 5525–5533,https://doi.org/10.1109/CVPR.2016.596.

  203. Yao C, Bai X, Liu W, Ma Y, Zhuowen Tu (2012) Detecting texts of arbitrary orientations in natural images, in 2012 IEEE conference on computer vision and pattern recognition, IEEE, 1083–1090,https://doi.org/10.1109/CVPR.2012.6247787.

  204. Ye Q, Doermann D (Jul. 2015) Text detection and recognition in imagery: a survey. IEEE Trans Pattern Anal Mach Intell 37(7):1480–1500.https://doi.org/10.1109/TPAMI.2014.2366765

    Article  Google Scholar 

  205. Yuan L, Lu F (2018) Real-time ear detection based on embedded systems, in 2018 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, 115–120,https://doi.org/10.1109/ICMLC.2018.8526987

  206. Yucel MK, Bilge YC, Oguz O, Ikizler-Cinbis N, Duygulu P, Cinbis RG (2018) Wildest Faces: Face Detection and Recognition in Violent Settings, [Online]. Available:http://arxiv.org/abs/1805.07566

  207. Yuliang L, Lianwen J, Shuaitao Z, Sheng Z (2017) Detecting curve text in the wild: new dataset and new solution, [Online]. Available:http://arxiv.org/abs/1712.02170.

  208. Zakria Z, Deng J, Kumar R, Khokhar MS, Cai J, Kumar J (2022) Multiscale and direction target detecting in remote sensing images via modified YOLO-v4. IEEE J Sel Top Appl Earth Obs Remote Sens 15:1039–1048.https://doi.org/10.1109/JSTARS.2022.3140776

    Article  Google Scholar 

  209. Zhang H, Hong X (2019) Recent progresses on object detection : a brief review, in Multimedia Tools and Applications, Multimedia Tools and Applications 78, no. June, 27809–27847,https://doi.org/10.1007/s11042-019-07898-2.

  210. Zhang L, Ma J (2021) Salient object detection based on progressively supervised learning for remote sensing images. IEEE Trans Geosci Remote Sens 59(11):9682–9696.https://doi.org/10.1109/TGRS.2020.3045708

    Article  Google Scholar 

  211. Zhang S, Benenson R, Schiele B (2017) CityPersons: a diverse dataset for pedestrian detection, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 4457–4465,https://doi.org/10.1109/CVPR.2017.474

  212. Zhang J, Xie Z, Sun J, Zou X, Wang J (2020, IEEE) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE access 8:29742–29754.https://doi.org/10.1109/ACCESS.2020.2972338

    Article  Google Scholar 

  213. Zhang X, Liu Y, Huo C, Xu N, Wang L, Pan C (2022) PSNet: perspective-sensitive convolutional network for object detection. Neurocomputing 468:384–395.https://doi.org/10.1016/j.neucom.2021.10.068

    Article  Google Scholar 

  214. Zhao Z-QQ, Zheng P, Xu S-TT, Wu X (2019, IEEE) Object detection with deep learning: A Review. IEEE Trans. Neural Networks Learn. Syst. 30(11):3212–3232.https://doi.org/10.1109/TNNLS.2018.2876865

    Article  Google Scholar 

  215. Zhao X, Zhang J, Tian J, Zhuo L, Zhang J (2021, Taylor & Francis) Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention. Int J Remote Sens 42(15):5764–5783.https://doi.org/10.1080/01431161.2021.1931537

    Article  Google Scholar 

  216. Zhou J, Yuqiao T, Li W, Wang R, Luan Z, Qian D (2019) LADet : A Light-weight and Adaptive Network for Multi-scale Object Detection, in Proceedings of The Eleventh Asian Conference on Machine Learning, 912–923.

  217. Zhu Y, Du J (2021, Elsevier) TextMountain: accurate scene text detection via instance segmentation. Pattern Recognit 110:107336.https://doi.org/10.1016/j.patcog.2020.107336

    Article  Google Scholar 

  218. Zhu Y, Jiang Y (2020, Elsevier BV) Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data. Image Vis Comput 104:104023.https://doi.org/10.1016/j.imavis.2020.104023

    Article  Google Scholar 

  219. Zhu H, Chen X, Dai W, Fu K, Ye Q, Jiao J (2015) Orientation robust object detection in aerial images using deep convolutional neural network, in 2015 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 3735–3739,https://doi.org/10.1109/ICIP.2015.7351502.

  220. Zhu Z, Liang D, Zhang S, Huang X, Li B, Hu S (2016) Traffic-sign detection and classification in the wild, in 2016 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, 2110–2118,https://doi.org/10.1109/CVPR.2016.232

  221. Zou Z, Shi Z (2018) Random access memories: a new paradigm for target detection in high resolution aerial remote sensing images. IEEE Trans Image Process 27(3):1100–1111.https://doi.org/10.1109/TIP.2017.2773199

    Article MathSciNet MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Punjabi University, Patiala, India

    Jaskirat Kaur

  2. Department of Computer Science and Engineering, Punjabi University, Patiala, India

    Williamjeet Singh

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  1. Jaskirat Kaur

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  2. Williamjeet Singh

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Jaskirat Kaur and Dr. Williamjeet Singh performed material preparation, data collection, and analysis. Jaskirat Kaur writes the first draft of the manuscript.

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Kaur, J., Singh, W. Tools, techniques, datasets and application areas for object detection in an image: a review.Multimed Tools Appl81, 38297–38351 (2022). https://doi.org/10.1007/s11042-022-13153-y

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