Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 12239))
Included in the following conference series:
1497Accesses
Abstract
Nowadays, the QR code is often used in many popular fields, such as payment and social networking. Therefore, it is particularly important to quickly and accurately detect the position of QR code in real complex scenes. Traditional QR code detection methods mainly use hand-engineered features for detection. However, the QR code photos we take may be blurred due to pixel, distance, and other problems, and may even produce some rotations and deformations because of the complex scenes. Under such circumstances, the traditional QR code detection methods may not be so applicable. Faster-RCNN was originally used for multiple object detection, but we adjusted it slightly and applied it to the detection of QR code. At the same time, we made a small dataset under complex scenes for training Faster-RCNN networks. However, in complex scenes, the size of the QR code vary greatly due to the distance of shooting, so we add an FPN module to the Faster-RCNN to improve the detection performance for small and multi-scale QR code. Experimental results show that our method has achieved good performances in the detection of QR code in complex scenes.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 16015
- Price includes VAT (Japan)
- Softcover Book
- JPY 20019
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abeles, P.: Study of QR code scanning performance in different environments. v3.https://boofcv.org/index.php?title=Performance:QrCode
Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng.29(6), 33–41 (1984)
Belussi, L., Hirata, N.: Fast QR code detection in arbitrarily acquired images. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 281–288. IEEE, Maceio, Alagoas (2011)
Chen, X., Gupta, A.: An implementation of faster RCNN with study for region sampling. arXiv preprintarXiv:1702.02138 (2017)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C., Soatto, S., Tomasi, C. (eds.) International Conference on Computer Vision & Pattern Recognition, vol. 1, pp. 886–893. IEEE Computer Society, San Diego (2005).https://hal.inria.fr/inria-00548512
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis.88(2), 303–338 (2010)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448. IEEE, Santiago, Chile (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE, Columbus (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell.37(9), 1904–1916 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegas (2016)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprintarXiv:1704.04861 (2017)
Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 650–657. IEEE, Washington, DC (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput.1(4), 541–551 (1989)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. IEEE, Honolulu (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46448-0_2
Pan, L., Qin, J., Chen, H., Xiang, X., Li, C., Chen, R.: Image augmentation-based food recognition with convolutional neural networks. CMC Comput. Mater. Continua59(1), 297–313 (2019)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprintarXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556 (2014)
Sun, X., Wu, P., Hoi, S.C.: Face detection using deep learning: an improved faster rcnn approach. Neurocomputing299, 42–50 (2018)
Szentandrási, I., Herout, A., Dubská, M.: Fast detection and recognition of QR codes in high-resolution images. In: Proceedings of the 28th Spring Conference on Computer Graphics, pp. 129–136. ACM, Budmerice (2013)
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis.104(2), 154–171 (2013)
Viola, P., Jones, M.: 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, p. I. IEEE, Kauai, HI (2001)
Wu, X., Luo, C., Zhang, Q., Zhou, J., Yang, H., Li, Y.: Text detection and recognition for natural scene images using deep convolutional neural networks. CMC Comput. Mater. Continua61(1), 289–300 (2019)
Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 443–457. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46475-6_28
Zhou, L., Wang, L., Chen, Y., Tang, Y.: Binaural sound source localization based on convolutional neural network. CMC Comput. Mater. Continua60(2), 545–557 (2019)
Author information
Authors and Affiliations
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China
Jinbo Peng, Song Yuan & Xin Yuan
Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430065, China
Jinbo Peng, Song Yuan & Xin Yuan
- Jinbo Peng
You can also search for this author inPubMed Google Scholar
- Song Yuan
You can also search for this author inPubMed Google Scholar
- Xin Yuan
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toSong Yuan.
Editor information
Editors and Affiliations
Nanjing University of Information Science, Nanjing, China
Xingming Sun
Nanjing University of Information Science, Nanjing, China
Jinwei Wang
Purdue University, West Lafayette, IN, USA
Elisa Bertino
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Peng, J., Yuan, S., Yuan, X. (2020). QR Code Detection with Faster-RCNN Based on FPN. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_38
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-57883-1
Online ISBN:978-3-030-57884-8
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative