关键词: YOLOv8, 小目标检测, 多尺度, 无人机, Wise-IoU (WIoU)

Abstract: Abstract: In view of low accuracy in small target detection from the current UAV perspective, a multi-scale small object detection algorithm for UAV perspective based on channel-prior-multi-scale cross-axis attention-YOLO（CPMS-YOLO） was proposed. Firstly, a multi-scale attention module of the algorithm named CPMS (Channel-Prior Multi-Scale Cross-Axis Attention) was incorporated into the backbone network, which is designed to better extract and emphasize useful features in complex backgrounds. In this case, it made the algorithm more easily learn the location details of the region of interest and improved the feature extraction ability of small targets at different scales in a complex context. Secondly, the backbone and feature fusion networks were restructured by adding a feature layer with the enriched small target semantic information, and the multi-scale MultiSEAM (Multi-scale Separated and Enhancement Attention Module) fusion module was adopted to complement contextual feature information for enabling better detection and recognition of small objects. And then, a C2f-RFE (C2f-Receptive Field Enhancement) module was designed to expand the receptive field of the feature map, which could provide for more accurate, faster, and multi-angle localization of target features, and thus enhancing small object detection. Finally, a loss function named WIoUv3 (Wise-IoUv3) was introduced to dynamically optimize the loss weights for small objects. The corresponding imbalance between positive and negative samples were overcome in the bounding box regression task, and further improved the algorithm’s detection performance for small target. The experimental results on the public VisDrone2019 dataset show that, compared to the baseline algorithm YOLOv8s, the proposed CPMS-YOLO based multi-scale small target detection algorithm improves precision, recall, mAP50, and mAP50-95 by 6.7, 5.8, 6.3, and 7.8 percentage points, respectively. It is evident that the channel-prior-multi-scale cross-axis attention-YOLO based algorithm can more accurately capture and recognize small targets.

Key words: YOLOv8, small target detection, Multi-scale, unmanned aerial vehicle, Wise-IoU (WIoU)