  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.
