Research on Vehicle Detection Method Based on Improved YOLOv5s

In response to the current challenge of simultaneously balancing detection speed and accuracy in vehicle detection, this study presents a research on an improved YOLOv5s-based vehicle detection method. By enhancing the existing model, we optimize the loss function of the training model and incorporate an attention mechanism. Additionally, a new dataset is established and trained. While ensuring swift vehicle recognition, we further enhance detection precision. Furthermore, a vehicle tracking module is introduced to the output of the vehicle detection, enabling prediction of the driving behavior of preceding vehicles. Through this research methodology, real-time vehicle detection and tracking are successfully achieved, yielding significant results when applied to vehicle-following systems. This approach not only enables rapid and accurate vehicle identification but also provides real-time monitoring of the driving conditions of vehicles ahead, issuing timely warnings to aid drivers in making necessary adjustments and thereby reducing the probability of rear-end accidents.