IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s
Yang Sun, Jiankun Song, Yong Li, Yi Li, Song Li, Zehao Duan
Abstract
Computer vision is now vital in intelligent vehicle environment perception systems. However, real-time small-scale pedestrian detection in intelligent vehicle environment perception systems is still needs to be improved. This paper proposes an intelligent vehicle-pedestrian detection method based on YOLOv5s, named IVP-YOLOv5, to use in vehicle environment perception systems. Based on the network structure of YOLOv5s, we replaced BottleNeck CSP with Ghost-Bottleneck to reduce the complexity of processing feature maps while maintaining good detection performance. To reduce the error between the ground truth box and the predicted box, we apply Alpha-IoU as the bounding box loss function, improving pedestrian detection accuracy and robustness. We introduce the slicing-aided hyper inference (SAHI) strategy, which enables the lightweight backbone network to capture more detailed features of pedestrians by enlarging image pixels. Experiments on the BDD100 K dataset show that the proposed IVP-YOLOv5 achieves 67.1% AP and 18.5% APs of pedestrian detection, and the GFLOPs and the number of parameters are only 10.5 and 4.9M.