Gaussian Synthesis for High-Precision Location in Oriented Object Detection

In aerial image scenes, the objects have properties of arbitrary orientation, large-scale range, and dense distribution. Thus, the object detector uses oriented bounding box (OBB) to locate objects, which is more complex and challenging than horizontal bounding box (HBB) detector. Mainstream OBB detectors mostly use one-to-many label assignment strategy to predict multiple bounding boxes for the same object, and filter out repeat predictions by non-maximum suppression (NMS). NMS ranks with confidence and drops the detection box with IoU higher than the threshold, which is easy to get the local optimum result. The clustered synthesis method gets more accurate results than the original NMS, but applying it to the OBB detector leads to border shift, which arises from the angular discontinuity problem. Therefore, we use Gaussian OBB (G-OBB) to deal with the angular discontinuity and thus eliminate the offset generated by direct synthesis. G-OBB is not an easy to understand and describe representation. For this reason, we analyze the properties of G-OBB, and design a decoding method to convert a G-OBB to a rotated rectangular box, further discussing its conditions. Based on the decoding method, we propose a Gaussian synthesis algorithm (GauS), which transforms the OBB into Gaussian space, followed by synthesis, and finally transforms the synthesis result back into a new OBB. We have derived the synthesis and decoding methods, and further verified their effectiveness. The extensive experiments on several existing models show that GauS takes very little computation and improves detector's high-precision performance. Extensive experiments verify the effectiveness, stability, and universality of the proposed algorithm. In addition, The RTMDet using GauS achieves a performance of 81.61 AP ₅₀ and gains a 0.39% improvement in mAP, which achieves the SOTA performance. Our implementation is available at: https://github.com/lzh420202/GauS.