Aircraft target detection in remote sensing images based on DFECANet
Aiming at the existing remote sensing image target detection methods with low detection accuracy for small-size aircraft targets,inaccurate feature information transfer and insufficient information interaction,a remote sensing image aircraft target detection method based on discriminative feature extraction and context-awareness is proposed.A backbone network with a discriminative feature extraction module is designed to enhance feature extraction for multi-scale aircraft targets;an adaptive feature enhancement module is introduced to selectively focus on small targets and optimize the transfer of feature information and information interaction;and a feature fusion up-sampling module is designed to perform up-sampling operations on the feature maps to improve the accuracy of high-level semantic information.The detection accuracy on the DOTAv1 dataset reaches 95.2%,which is 3.7%to 18%higher than that of mainstream algorithms such as YOLOv5s,SCRDet,ASSD.In addition,the detection speed and the number of model parameters of the proposed method are 147 frames per second and 13.4 M,respectively.Compared with the current mainstream algorithms,the proposed method has strong competitiveness and meets the real-time detection requirements of aircraft targets in the background of remote sensing.
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