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Samia35-2973 / Deep-Learning-for-Explainable-Traffic-Anomaly-Detection-in-Dhaka

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The project introduces a Multi-Stage Traffic Anomaly Analysis Framework for identifying and analyzing urban traffic congestion, particularly in Dhaka. Originally utilizing Faster R-CNN and DBSCAN, it has been upgraded to state-of-the-art YOLOv9e and YOLOv10l models for enhanced accuracy and efficiency.

computer-vision deep-learning traffic-analysis faster-rcnn object-detection vehicle-detection dhaka dbscan-clustering pedestrian-detection explainable-ai traffic-congestion congestion-analysis eigen-cam yolov9 yolov10 traffic-anomaly traffic-congestion-analysis congested-region-detection
  • Updated Feb 2, 2025
  • Jupyter Notebook

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