https://github.com/samia35-2973/deep-learning-for-explainable-traffic-anomaly-detection-in-dhaka

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 congested-region-detection congestion-analysis dbscan-clustering deep-learning dhaka eigen-cam explainable-ai faster-rcnn object-detection pedestrian-detection traffic-analysis traffic-anomaly traffic-congestion traffic-congestion-analysis vehicle-detection yolov10 yolov9

Last synced: 13 Mar 2025