A Plug And Play Fast Intersection Over Union Loss For Boundary Box Regression

Bounding box regression is a very effective method to improve the localization accuracy of object detection. Recently, the IoU-based regression losses have been widely used in object detection algorithms. However, we observe that they degenerate seriously in the late training period, leading to slow convergence and inaccurate localization. In this paper, we design a Fast Intersection over Union (FIoU) loss, which can not only keep the advantages but also solve the weakness of IoU-based losses. Furthermore, FIoU can be directly applied to Non-Maximum Suppression (NMS) as a criterion to improve the localization performance. Numerous experiments on two popular benchmark datasets show that our method is superior to other the-state-of-art methods.