ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection

Abstract

Existing oriented object detection in aerial images has progressed a lot in recent years and achieved a favorable success. However, high-precision oriented object detection in aerial images remains a challenging task. Some recent works have adopted the classification-based method to predict the angle in order to address boundary problem in angle. However, we have found that these works often neglect the sensitivity of objects with different aspect ratios to angle. At the same time, it is worth exploring a suitable way to improve the emerging transformer-based approaches in order to adapt them to oriented object detection. In this paper, we propose an Aspect Ratio Sensitive DEtection TRansformer, termed ARS-DETR, for oriented object detection in aerial images. Specifically, a new angle classification method, called Aspect Ratio aware Circle Smooth Label (AR-CSL), is proposed to smooth the angle label in a more reasonable way and discard the hyperparameter that introduced by previous work (e.g. CSL). Then, a rotated deformable attention module is designed to rotate the sampling points with the corresponding angles and eliminate the misalignment between region features and sampling points. Moreover, a dynamic weight coefficient according to the aspect ratio is adopted to calculate the angle loss. Comprehensive experiments on several challenging datasets demonstrate that our method achieves a competitive performance in the high-precision oriented object detection task.