Abstract: In order to solve the problems of low detection accuracy and missing detection in traditional YOLOv5 object detection algorithm， a small object detection method based on improved YOLOv5 was proposed. Firstly， to make anchor box better adapt to small targets， IOU （interp over union） is used to replace the Euclidean distance formula originally used in the K-means clustering process to redefine the distance between anchor box and ground truth. Secondly， a maximum pooling of 3×3 kernel size is added to spatial pyarmid pooling （SPP） to improve the detection accuracy of small targets. Finally， a data set containing a variety of small object is designed to verify the algorithm performance. Experimental results show that the mean average precision （mAP） of the improved YOLOv5 algorithm reaches 76.92%， which is 3.56 percentage points higher than that of the classical YOLOV5 algorithm. The detection performence is improved and missed object can be detected.

Key words: small object detection, YOLOv5, K-means clustering, spatial pyramid pooling, mean average precision