Using the improved YOLOv5 network to detect the real-time and effective position of milk and construct the milk data set

Abstract

Abstract In order to improve the detection speed of YOLOv5(You Only Look Once v5) in complex environments and dense target scenarios, a target detection method CN-YOLOv5(Cow Milk-You Only Look Once v5) improved YOLOv5 model is proposed. The traditional YOLOv5 network structure is improved, and the ability of the algorithm to extract features is improved by adding the SE (Squeeze and Excitation) attention module structure, and the accuracy of milk identification is improved. By improving the SPP (Spatial Pyramid Pooling) structure to SPPF (Spatial Pyramid Pooling Fast) structure, the detection speed is accelerated, and the CN-PAN (Cow Nipple Path Aggregation Network) model is proposed based on the PAN (Path Aggregation Network) module. Based on the PAN structure in the traditional YOLOv5 network, the iteration of small target detection is lightweight. Based on YOLOv5s, the milk image dataset CNmodel-YOLOV5s(Cow Milk model-You Only Look Once v5) was created. Experimental results show that the two algorithms can be tested before and after the improvement by using the milk dataset CNmodel-YOLOV5s. The improved algorithm on the test equipment increases the detection speed by up to 13% with almost no impact on accuracy. The improved YOLOV5 algorithm can identify milk targets more quickly, which provides theoretical support for subsequent detection of medium and large targets in complex environments and dense target scenarios.