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The detection of targets in aerial images is one of the hot spots in current research, and efficient and accurate detection has high value in military and civilian fields. However, due to the complex high-altitude environment and the variable scale and shape of air targets, the modification and coating of aircraft for different purposes make it difficult to detect air targets. Therefore, an improved first-order end-to-end air target detection algorithm is proposed. The algorithm adopts DATE-FCOS (Dual Assignment for End-to-End Fully Convolutional One-Stage Object Detection) as the basic framework, replaces GIoU with CIoU and adds it to the bounding box regression loss function, and on this basis, the deformable convolutional module is used to improve its backbone network and add CBAM ( Convolutional Block Attention Module). Through practical experimental tests, the proposed method improves the average detection accuracy of the FGVC aircraft dataset by 77.8%, which is 11% higher than the original model, which meets the application of aerial target detection.