An Improved Algorithm for Small Target Detection based on YOLO

Abstract

Aiming at the problems of small target size, dense target, missing detection and false detection in UAV aerial images, an improved YOLOv8s small target detection algorithm MS-P2-YOLO is proposed in this paper. First, through several initial convolution layers, the composite convolution module extracts the target feature information, and uses pooling operations of different scales to capture the global context information of the image. Then, the scale adaptive fusion unit module is used to scale and splicing the feature map in a certain form to integrate the feature information from different scales or layers. Then the multi-dimensional feature integration module is used to adjust the features according to the size or number of channels of the input feature map, and to enhance the feature representation through certain forms of scaling and sequence processing. At the same time, P2 detection head is added at the end to further increase the detection ability of small targets, which greatly improves the problems of missing detection, false detection and large number of parameters of small targets. Experiments show that compared with the VisDrone2019 dataset of MS-P2-YOLO and YOLOv8s, P, R and mAP50% have increased by 9%, 12.9% and 12.9% respectively, and the number of parameters has decreased by 4%. At the same time, generalization and comparison experiments were also conducted in YOLOv5s, YOLOv8s, ATSS, TOOD, and Father-RCNN, and the detection results were visualized. The experiments showed that all parameters were improved.