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.

通过监测车辆速度来实现交通安全，本研究集中在对象识别与车辆速度估计的监督学习应用，针对孟加拉国特定的交通条件和安全问题，提出了一种高效可行的解决方案，这项工作对该领域做出了显著贡献。