Advancing Traffic Safety in Dense Urban Settings: A Robust Real-Time Wrong-Way Driving Detection System with Hough Transform and YOLOv5

Abstract

In Bangladesh, where a burgeoning population strains limited road networks, wrong-way driving (WWD) exacerbates traffic congestion and elevates accident risks, posing a critical public safety challenge. We present a pioneering real-time WWD detection system that integrates cutting-edge image processing and deep learning to transform traffic monitoring. Our methodology combines the Hough line transform for precise road boundary extraction with the state-of-the-art YOLOv5 algorithm for high-fidelity, real-time vehicle detection. Vehicle direction is inferred from single-frame orientation (distinguishing front/rear views) without multi-frame tracking, ensuring lightweight operation and scalable deployment. Tested on a novel, region-specific dataset from Chattogram, the system achieves an impressive 96% vehicle detection accuracy ([email protected]) during training and validation. Real-world evaluations on 20 diverse test videos yield an overall 60.6% recall for wrong-way vehicles, with a representative subset of 10 videos reaching 75% recall (and 41% precision). This research advances computer vision applications in traffic safety, providing an effective and deployable framework for reducing WWD incidents in high congestion urban regions and paving the way for enhanced intelligent transportation systems.