Building a Bridge of Bounding Box Regression Between Oriented and Horizontal Object Detection in Remote Sensing Images
Xiaoliang Qian, Baokun Wu, Gong Cheng, Xiwen Yao, Wei Wang, Junwei Han
Abstract
Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation in remote sensing images. Up to now, most of bounding box regression (BBR) losses for OOD are transferred from horizontal object detection (HOD) methods, however, the transferring requires lots of professional knowledge and experiences for designers, consequently, many excellent BBR losses for HOD have not been transferred to OOD. To accelerate the research progress of BBR loss for OOD, a unified transferring strategy (UTS) is proposed to facilitate the transferring of BBR loss from HOD to OOD. The UTS proposes that the BBR of oriented bounding box (OBB) can be converted into the joint BBR of its horizontal smallest enclosing rectangle (HSER) and two offsets, so the BBR loss in HOD can be easily transferred to OOD by using HSER as a bridge. Following the UTS, a BBR loss named Rotated-IoU (RIoU) loss is designed for OOD by transferring an advanced BBR loss in HOD, which can be considered as an example to show how to transfer. On the basis of RIoU loss, a focal rotated-IoU (FRIoU) loss is proposed to assign larger weights to hard samples in the BBR. The comparisons with other BBR losses show that the RIoU and FRIoU losses can give better performance. The ablation study shows that giving more attention to hard samples in BBR is effective. The comparisons with many advanced methods demonstrate that the combinations of baseline methods and FRIoU loss achieve state-of-the-art performance on the DOTA and DIOR-R datasets.