  Bounding box regression plays a crucial role in the field of object
detection, and the positioning accuracy of object detection largely depends on
the loss function of bounding box regression. Existing researchs improve
regression performance by utilizing the geometric relationship between bounding
boxes, while ignoring the impact of difficult and easy sample distribution on
bounding box regression. In this article, we analyzed the impact of difficult
and easy sample distribution on regression results, and then proposed
Focaler-IoU, which can improve detector performance in different detection
tasks by focusing on different regression samples. Finally, comparative
experiments were conducted using existing advanced detectors and regression
methods for different detection tasks, and the detection performance was
further improved by using the method proposed in this paper.Code is available
at \url{https://github.com/malagoutou/Focaler-IoU}.
