Adaptive Multi-Level Feature Fusion and Attention-Based Network for Arbitrary-Oriented Object Detection in Remote Sensing Imagery

Abstract Compared with the classic object detection problem, detecting objects in aerial images has some special challenges including huge orientation variations, complicated and large background, and wide multi-scale distribution. Considering these three challenges together, we propose a novel arbitrary-oriented object detection framework consisting of three main parts. Firstly, the Cascading Attention Network (CA-Net) composed of a patching self-attention module and a supervised spatial attention module is proposed for enhancing the feature representations from objects of interest and suppressing the background noises in Feature Pyramid Network (FPN) from coarse to fine. Then, the Adaptive Feature Concatenate Network (AFC-Net) is proposed to adaptively stack the feature maps pooled from all FPN levels as well as the global semantic features, for dealing with the multi-scale change of objects. Lastly, the OBB Multi-Definition and Selection Strategy (OBB-MDS-Strategy) is proposed to regress rotated bounding boxes more smoothly and detect oriented objects more accurately in the training process. Our experiments are conducted on two common and challenging aerial datasets, i.e., DOTA and HRSC2016. Experiments results show that the proposed method has superior performances in multi-orientated objects detection compared with the representative methods.