Wrong-Way Vehicle Detection Using YOLOv7 for Enhanced Traffic Safety

James, Alphons Zacharia (2024) Wrong-Way Vehicle Detection Using YOLOv7 for Enhanced Traffic Safety. Masters thesis, Dublin, National College of Ireland.

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Abstract

Wrong-way driving is a significant contributor to road accidents and traffic congestion worldwide. Traditional methods for detecting wrong-way vehicles, such as manual monitoring, fixed cameras, or traffic sensors, have limitations in terms of real-time detection, scalability, and accuracy. These traditional systems often fail to provide timely alerts, especially in dynamic traffic conditions. This study addresses these gaps by implementing an advanced vehicle detection system using YOLOv7, which can accurately identify wrong-way drivers in real-time. So, the dataset used for this study is the ”Vehicle Detection” dataset, which contains a diverse set of images representing different vehicle types, including ambulances, buses, cars, motorcycles, trucks, and vans. The primary objective of this study is to develop a robust vehicle detection system capable of identifying and tracking vehicles in real-time video streams. Several models were explored for this task, including YOLOv5, YOLOv8, and YOLOv7, with each model trained and tested on the vehicle dataset. Among these, the best performance was achieved using YOLOv7, which demonstrated the highest mAP@0.50 score i.e. 0.876, making it the optimal model for this vehicle detection task. YOLOv7 outperformed other models in terms of accuracy and precision, particularly excelling in detecting various vehicle classes, such as cars and trucks, with higher precision and recall values. The study also included the implementation of a real-time detection system that tracks vehicles and identifies wrong-way driving violations using a reference direction vector and the system is evaluated based on assumptions using sample traffic videos.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics H Social Sciences > HE Transportation and Communications > Urban Transportation
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 14:31
Last Modified:	02 Sep 2025 14:31
URI:	https://norma.ncirl.ie/id/eprint/8713

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