Abstract: To enhance the recognition efficiency of UAVs in dark conditions and reduce missed detections and delays in complex environments and road conditions, this study proposes an improved YOLOv5s-GN-CB infrared image recognition method. This method enhances the efficiency of UAV infrared aerial images for detecting vehicles, people, and other types of targets. The main improvements to YOLOv5s achieved in this study include the following three aspects: 1) introducing the Ghost module into the YOLOv5s backbone network and incorporating NWD loss into Ghost; 2) adding the coordinate attention (CA) mechanism; 3) incorporating a weighted bidirectional feature pyramid network (BiFPN). The improved YOLOv5s-GN-CB detection model achieves an average accuracy of 95.1% (mAP@0.5) on the InfiRay infrared aerial photography man-vehicle detection dataset, with the FPS increased to 75.188 frames per second. Compared with the original YOLOv5 model, the average accuracy and FPS are improved by 4.2% and 12.02%, respectively. In the same scenario, the detection accuracy of UAV aerial photography infrared image target recognition has been significantly improved, and the delay rate has decreased.