Refined One-Stage Oriented Object Detection Method for Remote Sensing Images

Multi-class object detection in remote sensing images plays an important role in many applications but remains a challenging task because of scale imbalance and arbitrary orientations of the objects with extreme aspect ratios. In this paper, the Asymmetric Feature Pyramid Network (AFPN), Dynamic Feature Alignment (DFA) module, and Area-IoU regression loss are proposed on the basis of a one-stage cascaded detection method for the detection of multi-class objects with arbitrary orientations in remote sensing images. The designed asymmetric convolutional block is embedded into the AFPN for handling objects with extreme aspect ratios and improving the space representation with ignorable increases in calculation. The DFA module is proposed to dynamically align mismatched features, which are caused by the deviation between predefined anchors and arbitrarily oriented predicted boxes. The refined Area-IoU regression loss, which reconciles two new regression loss functions, the area-guided regression loss and IoU-guided regression loss, is proposed to simultaneously solve the scale imbalance problem and angle sensitivity problem. Experiments on three publicly available datasets, DOTA, HRSC2016, and ICDAR2015, show the effectiveness of the proposed method.