Remote Sensing Image Object Detection Based on Angle Classification

Arbitrarily-oriented object detection is a challenging task. Since the object orientation in remote sensing images is arbitrary, using horizontal bounding boxes will lead to low detection accuracy. Existing regression-based rotation detectors can lead to the problem of boundary discontinuity. In this paper, we propose a remote sensing image object detection method based on angle classification that uses rotation detection bounding boxes with angle information to detect objects. Specifically, we incorporate the neural architecture search framework with feature pyramid network (NAS-FPN) module in a dense detector (RetinaNet) and use a binary encoding method in angle classification. This method reduces the background influence, so that there is almost no overlap between detection boxes. Based on the angles of the detection boxes, we can infer the information of the motion direction of the target and further determine the motion trajectory of the target. We conducted ablation experiments on a large publicly available dataset for object detection in an aerial imagery (DOTA) dataset to verify the effectiveness of each module in the method and compared the method with several other detection methods. The experimental results demonstrate the effectiveness of our method.