- Divit Technology
- Azerbaijan
- 13:21
(UTC +03:00) - https://web.itu.edu.tr/ibrahimli21/
- https://orcid.org/0000-0002-2763-2871
- in/cavadibrahimli
Highlights
- Pro
CVML Engineer • Deep Learning • Real-Time AI Systems
Designing advanced perception & AI pipelines for vision-driven intelligence
I’mJavad Ibrahimli, aComputer Vision & Machine Learning Engineer (CVML) buildingAI-powered perception systems for industries likeautonomous driving, medical imaging, and advanced analytics.
I specialize inreal-time vision & deep learning pipelines, combining2D & 3D visual data with advanced ML architectures to deliveraccurate, scalable, and deployment-ready solutions.
💡“My mission is to make visual data intelligent & actionable through state-of-the-art AI.”
✅Computer Vision – Object Detection, Instance Segmentation, Feature Extraction
✅Deep Learning for Vision – Vision Transformers (ViT), CNNs, multi-modal perception
✅3D Vision & Scene Understanding – Point clouds, LiDAR/RGB fusion
✅Real-Time AI Optimization – CUDA, TensorRT, ONNX for embedded devices
✅Edge AI & Deployment – NVIDIA Jetson, optimized inference pipelines
Languages: Python, C++, CUDA
Frameworks: PyTorch, TensorFlow, OpenCV, TorchVision
Optimization & Deployment: ONNX, TensorRT, NVIDIA Jetson, Docker
✅Vision Transformer-based Lane Detection
Deep transformer architecture forrobust lane & road marking segmentation.
✅Real-Time Medical Image Segmentation (Edge AI)
OptimizedU-Net architecture deployable onlow-power edge devices for ultrasound imaging.
✅Multi-class Object Detection Pipeline
YOLOv8 & Detectron2-baseddetection + tracking for industrial applications.
✅High-Performance Image Stitching
Computer vision pipeline for seamlesspanorama generation using SIFT/ORB features.
🔗More CVML projects →GitHub Projects
💼Specialized in: Computer Vision • Deep Learning for Vision • Real-Time AI Systems
🌐Portfolio:javadibrahimli.github.io
💼LinkedIn:linkedin.com/in/cavadibrahimli
📄ResearchGate:researchgate.net/profile/Javad-Ibrahimli
📧Email:cenabibrahimov@gmail.com
I’m open to collaborations inDeep Learning for Computer Vision, Perception AI & Edge Deployment.
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Notes I took/used for passing Sensor Fusion Nanodegree
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