IC-GAP: Inscribed-Circle-Based Geometric Adaptive Processor for Elongated Object Detection in Remote Sensing

Oriented object detection in remote sensing images has achieved substantial progress, yet non-maximum suppression (NMS) remains vulnerable to angular variation when the overlap of high-aspect-ratio objects is measured with rotated bounding boxes. To address this problem, the inscribed-circle-based geometric adaptive processor (IC-GAP), a training-time geometric regularization framework for elongated object detection, is proposed. First, inscribed-circle intersection over union (IC-IoU) is introduced, which uses a rotation-invariant geometric core to provide a stable pairwise optimization signal. This alleviates the susceptibility of IoU to slight angular misalignments, which are particularly pronounced for high-aspect-ratio objects. Second, the polar aspect-stable (PAS) strategy is designed to generate bounded context-aware penalty weights from relative geometric relationships between object pairs. Third, IC-IoU and PAS are integrated into IC-GAP, which embeds adaptive NMS-oriented constraints into training and encourages prediction distributions that are more compatible with post-processing. Experiments on the Dataset for Object Detection in Aerial Images (DOTA) demonstrate that IC-GAP improves the mean average precision (mAP) by 1.8% over the baseline under the reported protocol.