Diag-IoU Loss for Object Detection
Shuangqing Zhang, Chenglong Li, Zhen Jia, Lei Liu, Zhang Zhang, Liang Wang
Abstract
Existing IoU-based loss functions have achieved promising performance for bounding box regression in object detection. However, they cannot fully reflect the relation between the predicted and target boxes in the case of box inclusions, and might thus deteriorate detection accuracy and efficiency. In this paper, we design a novel similarity measurement based on the box diagonal called Diag-IoU to well represent the divergence between the predicted and target boxes even in the case of box inclusions, and thus achieve superior localization accuracy and fast convergence. In particular, we equivalently represent a rectangular box with its box diagonal, which contains exclusive and informative geometrical factors, and define the Diag-IoU based on the similarities of a set of sampled point pairs from the predicted and target box diagonals. Based on the Diag-IoU, we design a general Diag-IoU loss, which can provide holistic information in measuring two boxes and thus differentiate the two boxes in the case of box inclusions. To validate the effectiveness of the proposed method, we apply the Diag-IoU loss to several representative object detectors, including YOLO v5s, Faster R-CNN, and FCOS. Extensive experiments on the synthetic data and two challenging object detection benchmark datasets, i.e., MS COCO and PASCAL VOC, demonstrate the superior performance of the proposed Diag-IoU loss compared to previous IoU-based losses as well as other metrics.