PVNet: A Used Vehicle Pedestrian Detection Tracking and Counting Method

Abstract

Advances in technology have made people’s lives more prosperous. However, the increase in the number of cars and the emergence of autonomous driving technology have led to frequent road accidents. Manual observation of traffic conditions requires high labor intensity, low work efficiency, and poses safety risks. The paper proposes a deep learning-based pedestrian-vehicle detection model to replace manual observation, overcoming human resource limitations and safety concerns. The model optimizes the darknet53 backbone feature extraction network, reducing parameters and improving feature extraction capabilities, making it more suitable for pedestrian-vehicle scenarios. In addition, the PVFPN multi-scale feature fusion method is used to facilitate information exchange between different feature layers. Finally, the Bytetrack method is used for target counting and tracking. The paper model shows excellent performance in pedestrian-vehicle detection and tracking in traffic scenarios. The experimental results show that the improved model achieves a mAP@.5 of 0.952 with only 32% of the parameters compared to YOLOv8s. Furthermore, the proposed PVNet model, combined with the Bytetrack method, maintains high detection accuracy and is applicable to pedestrian-vehicle detection and tracking in traffic scenarios. In summary, this section discusses the traffic issues arising from technological development and presents the optimization and performance of the deep learning-based pedestrian-vehicle detection model, along with its potential applications in traffic scenarios.