关键词: 无人机目标检测, YOLOv11, 双线性自注意力, MPDCIoU损失函数, AFPN检测头

Abstract: Aiming at the problems of various sizes, inadequate tiny target detection and dense overlapping of detection frames in air-to-ground detection of UAV, an improved YOLOv11 target detection network is proposed. The network replaces the C3K2 structure of YOLOv11 network with C3K2_D structure, which enhances the network's ability to extract irregular objects. Furthermore, the YOLOv11 network incorporates the bilinear self-attention mechanism. By combining local and non-local attention, the bilinear self-attention mechanism improves the backbone network's feature extraction capabilities. To address the issue of dense overlapping of detection frames, the MPDCIoU(Maximize position dependent combination Intersection over Union, MPDCIoU) loss function is designed to improve the regression accuracy of the bounding box. In addition, the AFPN(Asymptotic Feature Pyramid Network, AFPN) small target detection head is designed to improve the algorithm's detection performance for small targets. Experimental results show that the improved YOLOv11 network reaches 38.91% and 30.67% in mAP@0.5 and mAP@0.95 indicators, respectively, which are 1.79 percentage points and 1.76 percentage points higher than the YOLOv11 network. At the same time, the algorithm's frame rate reaches 124.6 frame/s, satisfying the real-time detection requirements. The improved YOLOv11 network presents advantages in terms of detection speed and accuracy when compared to representative target detection networks like RetinaNet, YOLOv7, and YOLOv8. By comparing the results of ablation experiments, it is found that the C3K2_D structure, the global nonlinear attention module, the MPDCIoU loss function, and the AFPN detection head can all successfully enhance the network's detection performance, the effectiveness of these four improved parts is demonstrated.

Key words: UAV target detection, YOLOv11, bilinear self attention, MPDCIoU loss function, AFPN detection head