  As an important component of the detector localization branch, bounding box
regression loss plays a significant role in object detection tasks. The
existing bounding box regression methods usually consider the geometric
relationship between the GT box and the predicted box, and calculate the loss
by using the relative position and shape of the bounding boxes, while ignoring
the influence of inherent properties such as the shape and scale of the
bounding boxes on bounding box regression. In order to make up for the
shortcomings of existing research, this article proposes a bounding box
regression method that focuses on the shape and scale of the bounding box
itself. Firstly, we analyzed the regression characteristics of the bounding
boxes and found that the shape and scale factors of the bounding boxes
themselves will have an impact on the regression results. Based on the above
conclusions, we propose the Shape IoU method, which can calculate the loss by
focusing on the shape and scale of the bounding box itself, thereby making the
bounding box regression more accurate. Finally, we validated our method through
a large number of comparative experiments, which showed that our method can
effectively improve detection performance and outperform existing methods,
achieving state-of-the-art performance in different detection tasks.Code is
available at https://github.com/malagoutou/Shape-IoU
