Dual-det: a fast detector for oriented object detection in aerial images

Fast and accurate object detection in aerial images remains a challenging task. Usually, to better describe an object, oriented bounding boxes (OBBs) can better fit objects. Due to high background complexity and large object scale variation, single-angle anchor-based two-stage detectors are widely adopted, which offer better accuracy. However, the single-angle prediction has a small error tolerance for objects with a large aspect ratio, and the hyperparameters of the anchor-based network are difficult to adjust, and the number of hyperparameters is extremely large. Furthermore, the two-stage detection inference speed is slow, and it is difficult to achieve real-time detection. In this paper, we propose Dual-Det, a keypoint-based oriented object detector. We firstly propose a dual-angle with a short-side and ratio regression strategy (DASR), which uses the object centre and the length and angles of two diagonals to represent an object. A short side guided (SSG) loss is further added to guide the direction of the diagonal regression box. To improve the detection performance for dense and tiny objects, a lightweight supervised pixel attention learner is finally proposed. The experiment results show that Dual-Det achieves 90.23 (Formula presented.) mAP at 46FPS on HRSC2016, 90.83 (Formula presented.) mAP at 46FPS on UCAS-AOD and 72.00 (Formula presented.) mAP at 0.018 s per image in the inference phase on DOTA. The code will be open source on https://github.com/gqy4166000/ijrs_dasr.