PSRR-MaxpoolNMS++: Fast Non-Maximum Suppression With Discretization and Pooling

Abstract

Non-maximum suppression (NMS) is an essential post-processing step for object detection. The de-facto standard for NMS, namely GreedyNMS, is not parallelizable and could thus be the performance bottleneck in object detection pipelines. MaxpoolNMS is introduced as a fast and parallelizable alternative to GreedyNMS. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster R-CNN. To address this issue, we observe that MaxpoolNMS employs the process of box coordinate discretization followed by local score argmax calculation , to discard the nested-loop pipeline in GreedyNMS to enable parallelizable implementations. In this paper, we introduce a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module to improve the above two stages, respectively. With these two modules, our PSRR-MaxpoolNMS is a generic and parallelizable approach, which can completely replace GreedyNMS at all stages in all detectors. Furthermore, we extend PSRR-MaxpoolNMS to the more powerful PSRR-MaxpoolNMS++ . As for box coordinate discretization , we propose Density-based Discretization for better adherence to the target density of the suppression. As for local score argmax calculation , we propose an Adjacent Scale Pooling scheme for mining out the duplicated box pairs more accurately and efficiently. Extensive experiments demonstrate that both our PSRR-MaxpoolNMS and PSRR-MaxpoolNMS++ outperform MaxpoolNMS by a large margin. Additionally, PSRR-MaxpoolNMS++ not only surpasses PSRR-MaxpoolNMS but also attains competitive accuracy and much better efficiency when compared with GreedyNMS. Therefore, PSRR-MaxpoolNMS++ is a parallelizable NMS solution that can effectively replace GreedyNMS at all stages in all detectors.