Bounding box regression is an important component in object detection. Recent work has shown the promising performance by optimizing the Intersection over Union (IoU) as loss. However, IoU-based loss has the gradient vanish problem in the case of low overlapping bounding boxes, and the model could easily ignore these simple cases. In this paper, we propose Side Overlap (SO) loss by maximizing the side overlap of two bounding boxes, which puts more penalty for low overlapping bounding box cases. Besides, to speed up the convergence, the Corner Distance (CD) is added into the objective function. Combining the Side Overlap and Corner Distance, we get a new regression objective function, Side and Corner Align Loss (SCALoss). The SCALoss is well-correlated with IoU loss, which also benefits the evaluation metric but produces more penalty for low-overlapping cases. It can serve as a comprehensive similarity measure, leading the better localization performance and faster convergence speed. Experiments on COCO and PASCAL VOC benchmarks show that SCALoss can bring consistent improvement and outperform $\ell_n$ loss and IoU based loss with popular object detectors such as YOLOV3, SSD, Reppoints, Faster-RCNN.

本研究提出的Side Overlap（SO）和Corner Distance（CD）的组合得出了一个新的回归目标函数，即Side and Corner Align Loss（SCALoss），在COCO、PASCAL VOC和LVIS基准测试中比l_n损失和IoU损失更具优势，能够提高定位性能和优化收敛速度。

SCALoss: 边角对齐的边界框回归损失函数