Research on Air Target Detection Based on DATE-FCOS
Target detection in aerial images is one of the current research hotspots,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 tar-gets,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 dual assignment for end-to-end fully convolutional one-stage object detection(DATE-FCOS)as the basic framework,replaces the generalizedi intersection over union(GIoU)with the complete intersection over union(CIoU),and adds it to the bounding box regression loss function.On this basis,the deformable con-volutional module is used to improve its backbone network and add the convolutional block attention module(CBAM)after the FPN structure.Through practical experimental tests,the proposed method improves the average detection accuracy of the fine-grained vis-ual cassification of aircraft(FGVC)aircraft dataset by 77.8％,and the average detection accuracy of the proposed method is 11％higher than that of the original model,which meets the application of aerial target detection.
万方数据
维普
