Control Distance IoU and Control Distance IoU Loss for Better Bounding Box Regression

Abstract

Numerous improvements in feedback mechanisms have contributed to the great progress in object detection. In this paper, we first present an evaluation-feedback module, which consists of an evaluation system and feedback mechanism. Then we analyze and summarize traditional evaluation-feedback modules. We focus on both the evaluation system and the feedback mechanism, and propose Control Distance IoU and Control Distance IoU loss function (CDIoU and CDIoU loss) without increasing parameters in models, which make significant enhancements on several classical and emerging models. Finally, we propose Automatic Ground Truth Clustering (AGTC) and Floating Learning Rate Decay (FLRD) for faster regression in object detection. Experiments show that a coordinated evaluation-feedback module can effectively improve model performance. Both CNN and transformer-based detectors with CDIoU + CDIoU loss, AGTC, and FLRD achieve excellent performances. There are a maximum AP improvement of 2.9%, an average AP of 1.1% improvement on MS COCO, a maximum AP improvement of 8.2%, and an average AP improvement of 3.7% on Visdrone dataset.