Bounding box regression (BBR) has been widely used in object detection and
instance segmentation, which is an important step in object localization.
However, most of the existing loss functions for bounding box regression cannot
be optimized when the predicted box has the same aspect ratio as the
groundtruth box, but the width and height values are exactly different. In
order to tackle the issues mentioned above, we fully explore the geometric
features of horizontal rectangle and propose a novel bounding box similarity
comparison metric MPDIoU based on minimum point distance, which contains all of
the relevant factors considered in the existing loss functions, namely
overlapping or non-overlapping area, central points distance, and deviation of
width and height, while simplifying the calculation process. On this basis, we
propose a bounding box regression loss function based on MPDIoU, called LMPDIoU
. Experimental results show that the MPDIoU loss function is applied to
state-of-the-art instance segmentation (e.g., YOLACT) and object detection
(e.g., YOLOv7) model trained on PASCAL VOC, MS COCO, and IIIT5k outperforms
existing loss functions.

提出了一种基于最小点距离的新型边框相似性比较度量方法 MPDIoU，将所有现有损失函数中考虑的相关因素包含在内，同时简化了计算过程，并基于此提出了一种基于 MPDIoU 的边框回归损失函数 LMPDIoU，实验结果表明，在 PASCAL VOC、MS COCO 和 IIIT5k 上训练的基于 MPDIoU 损失函数的实例分割（如 YOLACT）和对象检测（如 YOLOv7）模型优于现有损失函数。