  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 (http://bit.ly/2nJLNMu).
