Exploration of Vehicle Target Detection Method Based on Lightweight YOLOv5 Fusion Background Modeling

Abstract

Due to the explosive increase per capita in vehicle ownership in China brought about by the continuous development of the economy and society, many negative impacts have arisen, making it necessary to establish the smart city system that has rapidly developing vehicle detection technology as its data acquisition system. This paper proposes a lightweight detection model based on an improved version of YOLOv5 to address the problem of missed and false detections caused by occlusion during rush hour vehicle detection in surveillance videos. The proposed model replaces the BottleneckCSP structure with the Ghostnet structure and prunes the network model to speed up inference. Additionally, the Coordinate Attention Mechanism is introduced to enhance the network’s feature extraction and improve its detection and recognition ability. Distance-IoU Non-Maximum Suppression replaces Non-Maximum Suppression to address the issue of false detection and omission when detecting congested targets. Lastly, the combination of the five-frame differential method with VIBE and MD-SILBP operators is used to enhance the model’s feature extraction capabilities for vehicle contours. The experimental results show that the proposed model outperforms the original model in terms of the number of parameters, inference ability, and accuracy when applied to both the expanded UA-DETRAC and a self-built dataset. Thus, this method has significant industrial value in intelligent traffic systems and can effectively improve vehicle detection indicators in traffic monitoring scenarios.