2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Oct. 22, 2017 to Oct. 29, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.593
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 VOC2007 (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.
computational complexity, computer vision, learning (artificial intelligence), neural nets, object detection
N. Bodla, B. Singh, R. Chellappa and L. S. Davis, "Soft-NMS — Improving Object Detection with One Line of Code," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2018, pp. 5562-5570.