It has been a long history that most object detection methods obtain objects
by using the non-maximum suppression (NMS) and its improved versions like
Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based
methods from three aspects: 1) The bounding box with highest confidence value
may not be the true positive having the biggest overlap with the ground-truth
box. 2) Not only suppression is required for redundant boxes, but also
confidence enhancement is needed for those true positives. 3) Sorting candidate
boxes by confidence values is not necessary so that full parallelism is
achievable.
In this paper, inspired by belief propagation (BP), we propose the Confidence
Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully
parallelizable as well as better in accuracy. In CP-Cluster, we borrow the
message passing mechanism from BP to penalize redundant boxes and enhance true
positives simultaneously in an iterative way until convergence. We verified the
effectiveness of CP-Cluster by applying it to various mainstream detectors such
as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO
show that our plug and play method, without retraining detectors, is able to
steadily improve average mAP of all those state-of-the-art models with a clear
margin from 0.3 to 1.9 respectively when compared with NMS-based methods.

本文提出了一种基于置信传播聚类（CP-Cluster）的物体检测方法，它采用了置信度信息的信息传递机制，能够同时去除冗余的检测框并增强真正的正类检测框。与传统的基于 NMS 的方法相比，在不重新训练检测器的情况下，我们的方法能够显著提高各种主流检测器的平均精度。