UAV detection algorithm based on attentional feature fusion

An improved unmannd aerial vehicle (UAV) detection algorithm, STC-YOLOv5 is proposed to address the problem of failing to quickly and accurately identify UAV targets under complex environmental conditions, such as similar colors of the background and UAVs, and overlapping of target occlusions. The backbone feature extraction network of STC-YOLOv5 employs Swin Transformer to enhance the robustness of the network against complex environments. The YOLOv5 model feature fusion network incorporates the convolution block attention module (CBAM) to decrease superfluous feature attention and increase attention on the UAV target’s key features. The loss function is optimized according to the characteristics of UAVs, and angle loss, distance loss and shape loss are introduced into the complete-IoU (CIoU) loss function, which improves the recognition accuracy of occluded UAV targets. In the case of partially occluded UAV targets, the improved STC-YOLOv5 algorithm has an average precision of 92.98% and a recall of 87.09%, which are 2.88% and 6.03% higher than the YOLOv5 algorithm, respectively. The results of experimental validation on the independently established UAV flight dataset demonstrate that the algorithm can achieve quick and precise UAV recognition in challenging scenarios.