  Numerous improvements for feedback mechanisms have contributed to the great
progress in object detection. In this paper, we first present an
evaluation-feedback module, which is proposed to consist of evaluation system
and feedback mechanism. Then we analyze and summarize the disadvantages and
improvements of traditional evaluation-feedback module. Finally, we focus on
both the evaluation system and the feedback mechanism, and propose Control
Distance IoU and Control Distance IoU loss function (or CDIoU and CDIoU loss
for short) without increasing parameters or FLOPs in models, which show
different significant enhancements on several classical and emerging models.
Some experiments and comparative tests show that coordinated
evaluation-feedback module can effectively improve model performance. CDIoU and
CDIoU loss have different excellent performances in several models such as
Faster R-CNN, YOLOv4, RetinaNet and ATSS. There is a maximum AP improvement of
1.9% and an average AP of 0.8% improvement on MS COCO dataset, compared to
traditional evaluation-feedback modules.
