Bow Direction Detection Based on Angular Coding With Heading Intersection Over Union Loss

Yaxiong Chen, Jiang Liu, Qiangqiang Huang, Hao Sun, Shengwu Xiong, Xiaoqiang Lu

Abstract: Accurate bow direction detection is essential for ship trajectory prediction and port monitoring. Existing ship detection networks typically output angles within 180°, while extending to 360° introduces cyclic issues affecting rotation intersection over union (RIoU) accuracy. This study proposes a novel bow direction detection algorithm that extends network output to 360° and integrates a heading intersection over union (HIoU) loss to enhance detection accuracy and robustness. Additionally, an HIoU loss function is designed to improve bow direction identification and reduce quantization errors in hash codes. The algorithm is evaluated on three datasets: FGSD, OHD-SJTU-S, and OHD-SJTU-L. On FGSD, it achieves mean average precision (mAP) of 91.14%. On OHD-SJTU-S, it attains an $\text {mAP}_{50:95}$ of 63.3% and a bow direction prediction accuracy of 90.7%. On OHD-SJTU-L, the $\text {mAP}_{50:95}$ is 29.2%, with an accuracy of 80.2%.