VODet: A Vertex Offset-Based Method for Oriented Object Detection in Remote Sensing Images

To tackle the challenges in oriented object detection, such as discontinuous angle regression, low precision in discrete classification, and high complexity in probabilistic modeling, this paper proposes VODet (Vertex Offset-based Detector), a novel oriented object detection method. VODet models the geometric structure of rotated objects through a coupled vertex offset mechanism, inherently avoiding boundary discontinuity. Specifically, it first regresses the horizontal enclosing rectangle of a target from horizontal anchors, then predicts only two offset ratios to reconstruct the four vertices of the rotated bounding box. For near-horizontal critical cases, a lightweight direction classifier adaptively distinguishes horizontal boxes from rotated ones, reducing regression complexity and improving robustness. To further enhance feature representation, we design a hierarchical special attention model (HSAM) based on Swin Transformer blocks, which effectively captures both local details and global contextual dependencies. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD demonstrate that VODet achieves competitive detection accuracy (80.44% mAP on DOTA, 90.42% AP on HRSC2016, and 90.11% mAP on UCAS-AOD), with particular advantages for objects of large aspect ratios and dense arrangements. Moreover, VODet achieves the highest inference speed (26.7 FPS) and the lowest computational complexity (192.3 GFLOPs) among the compared methods, demonstrating a favorable accuracy-efficiency trade-off. These results confirm the effectiveness and practical value of the proposed method.