An Anchor-Free and Angle-Free Detector for Oriented Object Detection Using Bounding Box Projection

The detection and recognition of oriented objects in remote sensing images is a challenging task due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. Many oriented object detection algorithms need to obtain accurate angles or adopt anchors to predict the oriented bounding boxes. When directly predicting the angles of objects’ oriented bounding boxes, the loss of angle is discontinuous during training, which makes it difficult to obtain accurate boundary of oriented objects. And the anchors also aggravate the problems of class imbalance and computational cost. To address the above problems, this paper proposes an anchor-free and angle-free detector called AF2Det. AF2Det adopts the information of the bounding box projection instead of the angle to represent and reconstruct the object’s oriented bounding boxes, which could avoid the problem of boundary discontinuity. To predict the information of the bounding box projection, an anchor-free architecture is built to predict objects as points based on a simple but strong U-shaped architecture. And the deformable convolution and the bottom-up feature fusion method are integrated effectively to enhance AF2Det’ s capacity for objects’ shapes, orientations, and scales. The extensive experiments are conducted on multiple datasets, i.e., HRSC2016, FGSD2021, DOTA, and RSDD-SAR to validate the effectiveness of our method. The experimental results demonstrate that the proposed AF2Det outperforms other anchor-free algorithms and obtains competitive results on oriented object detection.