Focaler-IoU: More Focused Intersection over Union Loss
This work addresses the issue of positioning accuracy in object detection for computer vision applications, but it appears incremental as it builds on existing geometric relationship methods.
The paper tackles the problem of bounding box regression in object detection by proposing Focaler-IoU, a loss function that focuses on difficult and easy sample distribution, resulting in improved detection performance across different tasks as shown in comparative experiments.
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}.