Accurate identification and positioning of aircraft targets is the key to the victory of aviation safety and information war. In view of the problems that traditional aircraft target identification has poor anti-interference perform- ance and is sensitive to occlusion, illumination and scale and difficult to cope with complex scene requirements, an aircraft target detection algorithm based on improved YOLOv5 is proposed. IOU-NWD Similarity Metric for Bounding Boxes solves the ambiguity of label assignment for aircraft small targets by IOU mechanism. Using GFPN based on NL- net module, the " cross-layer" and " cross-scale" adaptive fusion is completed, and more abundant and representative characteristic information is obtained. soft-NMS method is used to solve the problem of missing detection of small air- craft targets in crowded target areas. The experimental results show that compared with the original YOLOv5, the Pre- cision, Recall, mAP0. 5 and MAP0. 5:0. 95 of the improved model are increased by 1. 9%, 10. 4%, 3. 6% and 5. 8%, respectively. Through targeted network adjustment and module migration, the algorithm can improve the detec- tion effect of the model on small and blocked aircraft targets. The superiority of the algorithm is verified by experi- ments. The experimental results show that AIR-YOLO is superior to YOLOv5 in terms of detection accuracy and ro- bustness, which solves the problem of false detection of small aircraft targets in the original YOLOv5 algorithm.