Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle

The YOLOv4 network is widely used in object detection tasks as a representative network, but there is also the problem that the complexity of the network model affects the detection speed. In this paper, we propose an improved MV2_S_YE object detection algorithm based on the YOLOv4 network to improve the detection accuracy while increasing the road object detection speed. Firstly, the backbone network CSPDarknet53 of the YOLOv4 network is replaced by the Mobilenetv2 network to reduce the number of parameters of the network; secondly, the channel attention mechanism is introduced, and the SENet module is embedded in the structure of the PANet to optimize the object detection accuracy; finally, the EIOU loss function is used to replace the CIOU loss function to improve the object detection accuracy further. The MV2_S_YE network is obtained and tested on Pascal VOC, Udacity, and KAIST datasets. To evaluate our approach, we compared MV2-S-YE with YOLOv4, YOLOv4-tiny, YOLOv7-tiny and YOLOv8s. The results show that MV2-S-YE mAP@0.5 achieves 80.9%, 66.7%, and 94.8% on the VOC2007, Udacity, and KAIST test sets, respectively, and is higher than YOLOv8s on both the Udacity and KAIST test sets. On the VOC2007 test set MV2-S-YE achieves a detection speed of 45FPS which is higher than YOLOv8s.