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

翻译：作为检测器定位分支的重要组成部分，边界框回归损失在目标检测任务中起着关键作用。现有的边界框回归方法通常考虑真实框与预测框之间的几何关系，通过边界框的相对位置和形状计算损失，却忽略了边界框自身形状与尺度等固有属性对回归过程的影响。为弥补现有研究的不足，本文提出一种聚焦于边界框自身形状与尺度的回归方法。首先，我们分析了边界框的回归特性，发现边界框自身的形状与尺度因素会对回归结果产生影响。基于上述结论，我们提出了Shape IoU方法，该方法通过关注边界框自身的形状与尺度计算损失，从而使边界框回归更加精确。最后，我们通过大量对比实验验证了该方法，结果表明我们的方法能有效提升检测性能，优于现有方法，在不同检测任务中均达到了最先进的水平。代码已开源至https://github.com/malagoutou/Shape-IoU 。