Real-time detection algorithm for non-motorized vehicles based on D-YOLO model

In complex traffic scenarios, it is crucial to develop a rapid and precise real-time detection system for non-motorized vehicles to ensure safe driving. D-YOLO is a lightweight real-time detection technique for non-motorized vehicles based on an enhanced version of YOLOv4-tiny. Typically, the computing capabilities of mobile devices are constrained, therefore we begin by reducing the number of model parameters. Then, we add dilated convolution and depthwise separable convolution into the network’s Cross Stage Partial Connection (CSPNet) in order to produce the DCSPNet with improved performance. Coordinate Attention(CA) is implemented to enhance the network’s capability to extract effective features. In the neck network of the model by introducing a spatial pyramid set (SPP) to enhance the feature representation of non-motorized vehicles in the feature layer. Finally, we test this proposed model on dataset, the experimental results show that D-YOLO has a model size of only 6.7MB, which is 16.5MB smaller than YOLOv4-tiny. The detection speed of D-YOLO is about 25% faster than that of YOLOv4-tiny, D-YOLO has approximately 58% fewer model parameters than YOLOv4-tiny, D-YOLO has a mAP of 70.36%, which is 2.01% higher than YOLOv4-tiny. It can be shown that D-YOLO ensures both accuracy and real-time performance to satisfies the demand of real-time detection of non-motorized vehicles in intelligent traffic scenarios.