Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention

The objects in remote sensing images have large-scale variations, arbitrary directions, and are usually densely arranged, and small objects are easily submerged by background noises. They all hinder accurate object detection. To address the above problems, this paper proposes an object detection method combining feature enhancement and hybrid attention. Firstly, a feature enhancement fusion network (FEFN) is designed, which carries out dilated convolution with different dilation rates acting on the multi-layer features, and thus fuses multi-scale, multi-receptive field feature maps to enhance the original features. FEFN obtains more robust and discriminative features, which adapt to various objects with different scales. Then, a hybrid attention mechanism (HAM) module composed of pixel attention and channel attention is proposed. Through context dependence and channel correlation, introduced by pixel attention and channel attention respectively, HAM can make the network focus on object features and suppress background noises. Finally, this paper uses box boundary-aware vectors to determine the locations of objects and detect the arbitrary direction objects accurately, even if they are densely arranged. Experiments on public dataset DOTA show that the proposed method achieves 75.02% mAP, showing an improvement of 2.7% mAP compared with BBAVectors.

Automated summary

This paper proposes an object detection method combining feature enhancement and hybrid attention to address the challenges in object detection in remote sensing images. The proposed method includes a feature enhancement fusion network and a hybrid attention mechanism, which improve the robustness and discriminability of features and focus on object features while suppressing background noises. Experimental results on the DOTA dataset demonstrate the effectiveness of the proposed method.