PWIoU-YOLO: A Position-Wise IoU Optimized Detector with Multi-Scale Context Fusion for Aerial Oriented Objects

Accurate oriented object detection in aerial imagery is crucial for remote sensing applications, yet remains challenging due to arbitrary orientations, extreme aspect ratios, and dense distributions of objects. Existing rotated detectors often suffer from unstable training gradients and inadequate geometric representation in Intersection over Union (IoU) computation. This study proposes PWIoU-YOLO, a novel detection framework that introduces position-wise IoU (PWIoU) as the core optimization str