Non-maximum suppression is an integral part of the object detection pipeline.
First, it sorts all detection boxes on the basis of their scores. The detection
box M with the maximum score is selected and all other detection boxes with a
significant overlap (using a pre-defined threshold) with M are suppressed. This
process is recursively applied on the remaining boxes. As per the design of the
algorithm, if an object lies within the predefined overlap threshold, it leads
to a miss. To this end, we propose Soft-NMS, an algorithm which decays the
detection scores of all other objects as a continuous function of their overlap
with M. Hence, no object is eliminated in this process. Soft-NMS obtains
consistent improvements for the coco-style mAP metric on standard datasets like
PASCAL VOC 2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for
R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any
additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves
state-of-the-art in object detection from 39.8% to 40.9% with a single model.
Further, the computational complexity of Soft-NMS is the same as traditional
NMS and hence it can be efficiently implemented. Since Soft-NMS does not
require any extra training and is simple to implement, it can be easily
integrated into any object detection pipeline. Code for Soft-NMS is publicly
available on GitHub (this http URL).

Soft-NMS 算法通过将检测框与最大得分检测框的重叠度作为连续函数的信号降低其得分以替代一般的 NMS 算法进行非极大值抑制，从而提高了目标检测的效果和精度，不需要额外训练，且与常规 NMS 算法的计算复杂度相同。