Vehicle Speed Detection System Utilizing YOLOv8: Enhancing Road Safety and Traffic Management for Metropolitan Areas

TL;DR

The study addresses the need for accurate, real-time vehicle speed monitoring to improve road safety in Bangladesh. It introduces a YOLOv8-based speed estimation pipeline that uses ROI-focused detection, homography-based perspective transformation, and frame-rate–driven distance-time calculations, achieving MAE = 3.5 and RMSE = 4.22 on ground-truth data. The approach demonstrates robust performance under diverse traffic conditions and is presented as a scalable, cost-effective alternative to conventional radar or camera methods, with potential for automated incident alerting. This work contributes a practical framework for urban traffic management where real-time speed data can inform enforcement and infrastructure planning.

Abstract

In order to ensure traffic safety through a reduction in fatalities and accidents, vehicle speed detection is essential. Relentless driving practices are discouraged by the enforcement of speed restrictions, which are made possible by accurate monitoring of vehicle speeds. Road accidents remain one of the leading causes of death in Bangladesh. The Bangladesh Passenger Welfare Association stated in 2023 that 7,902 individuals lost their lives in traffic accidents during the course of the year. Efficient vehicle speed detection is essential to maintaining traffic safety. Reliable speed detection can also help gather important traffic data, which makes it easier to optimize traffic flow and provide safer road infrastructure. The YOLOv8 model can recognize and track cars in videos with greater speed and accuracy when trained under close supervision. By providing insights into the application of supervised learning in object identification for vehicle speed estimation and concentrating on the particular traffic conditions and safety concerns in Bangladesh, this work represents a noteworthy contribution to the area. The MAE was 3.5 and RMSE was 4.22 between the predicted speed of our model and the actual speed or the ground truth measured by the speedometer Promising increased efficiency and wider applicability in a variety of traffic conditions, the suggested solution offers a financially viable substitute for conventional approaches.

Figures (8)

• Figure 1: Porposed Methodology of Speed Detection System
• Figure 2: YOLOv8 Network Architecture
• Figure 3: Region of Interest ROIs for the first road
• Figure 4: Region of Interest ROIs for the second road
• Figure 5: Source and Target Identification