A review of non-maximum suppression algorithms for deep learning target detection

Deep learning methods have been more and more widely applied in the field of target detection. As an important part of deep learning target detection, non-maximum suppression is used to eliminate redundant detection bounding boxes generated during target detection and find out the optimal target boundary boxes, so as to speed up detection efficiency and improve detection accuracy. This article first introduces the related concepts and computational principles of traditional non-maximum suppression algorithm, and points out its problems. Based on this, the Soft-NMS, Softer-NMS, IOU-Guided NMS, Adaptive NMS and DIOU-NMS, a total of 5 kinds of improved maximum suppression algorithm principle is introduced and comparative analysis. And then we summarize the advantages and disadvantages of various algorithms. Finally, in view of the common problems existing in each algorithm, this paper points out the direction for improvement of non-maximum suppression algorithm, and provides technical reference and support for researchers in related fields.

Automated summary

The article introduces the application of deep learning in target detection, focusing on the principles and comparative analysis of the non-maximum suppression algorithm and five improved algorithms. Finally, improvement directions are proposed to provide technical reference and support for researchers in related fields.