Vehicle distance warning method based on improved YOLOv4-tiny algorithm
Aiming at the problem that the existing network is difficult to accurately recognize the target of road detection and vehicle distance in real time,a vehicle distance warning method based on improved YOLOv4-tiny algorithm was proposed.Firstly,the feature extraction structure of YOLOv4-tiny algorithm was summarized,and the shortcomings of the original network structure were analyzed.Secondly,SPPF of spatial pyramid pooling layer was added to the original network to further extract target features and enhance the ability to express deep semantic information.Feature Pyramid Network(FPN)structure was added with down-sampling channel and CSPnet layer to fully integrate multi-scale image features and avoid the loss of shallow information.Finally,the Mosaic data enhancement method was used to enrich the data set training samples,and the improved YOLOv4-tiny algorithm was combined with the principle of single visual distance detection.The vehicle distance warning experiment was conducted by setting three levels of information cues according to the vehicle distance.The results show that the detection speed of the proposed algorithm on PASCAL VOC dataset is 43 frames/s,and the average accuracy is 81.25%,which is 3.59%higher than that of YOLOv4-tiny algorithm.It can be seen that the improved YOLOv4-tiny algorithm has good target detection accuracy while meeting the real-time requirements of detection,which has guiding significance for improving the application effect of vehicle distance warning method.
engineering of communications and transportation safetytarget detectionYOLOv4-tiny algorithmmonocular distance measurementvehicles warning
NETL
NSTL
万方数据
