Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh
This work addresses vehicle detection for traffic surveillance in Bangladesh, an incremental contribution as it applies existing methods to a new dataset.
The study tackled vehicle detection in Bangladeshi traffic images by analyzing YOLO-based architectures, finding YOLOV5x to be the best with improvements of 7% and 4% in mAP and 12% and 8.5% in accuracy over YOLOv3 and YOLOV5s, respectively.
The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.