Gaussian guided IoU: A better metric for balanced learning on object detection

Abstract

Most anchor-based detectors use intersection over union (IoU) to assign targets to anchors during training. However, IoU did not pay enough attention to the proximity of the anchor's centre to the centre of the truth box, resulting in two issues: (1) the most slender objects were given just one anchor, resulting in insufficient supervision information for slender objects during training; (2) IoU cannot accurately represent the degree of alignment between the feature's receptive field at the anchor's centre and the object. As a result, some features with good alignment degrees are missing, while others with poor alignment degrees are used, reducing the model's localisation accuracy. To address these issues, we first created a Gaussian Guided IoU (GGIoU), which prioritises the proximity of the anchor's centre to the truth box's centre. We then proposed GGIoU-balanced learning methods, including GGIoU-guided assignment strategy and GGIoU-balanced localisation loss. This method can assign multiple anchors to each slender object, favouring features that are well-aligned with the objects during the training process. A large number of experiments show that GGIoU-balanced learning can solve the aforementioned problems and significantly improve the detection model's performance.