Direction Prediction Redefinition: Transfer Angle to Scale in Oriented Object Detection

Abstract

Oriented object detection has garnered significant attention. However, rotational symmetry and discontinuity at boundaries can confuse networks, leading to discontinuous loss and regression inconsistency. In this paper, we propose an efficient multi-directional object detection framework named Direction Prediction Redefinition (DPR). We describe the angle variation of rotated bounding boxes ( $B_{r}$ ) as changes in the dimensions of horizontal bounding boxes ( $B_{h}$ ). Specifically, we generate two sets of horizontal bounding boxes by predicting the center points of the corresponding boundaries within the rotated bounding box, thereby avoiding boundary issues caused by angle prediction. To further achieve robust rotated boundary representation, we propose the Joint Scale Representation method and the State Feature Encoding module, which are used to eliminate outliers in rotated boundaries and guide the correct selection of horizontal bounding box vertices, respectively. Moreover, we further abstract DPR as Multiple Trigonometric functions based DPR (DPR-MT). This method maps a single angle into four sets of trigonometric functions and considers them as the four sides of the horizontal bounding box. This approach predicts angles in the form of horizontal bounding boxes without complex operations, making it plug-and-play. Experimental results and visual analysis on challenging datasets further verify the effectiveness and competitiveness of our proposed method.