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    We introduce PSRR-MaxpoolNMS and PSRR-MaxpoolNMS++, novel parallelizable non-maximum suppression (NMS) methods. These approaches effectively replace the standard GreedyNMS across all object detection stages, offering improved efficiency and accuracy.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Non-maximum suppression (NMS) is critical for object detection but the standard GreedyNMS algorithm is a performance bottleneck due to its non-parallelizable nature.
    • Existing parallelizable alternatives like MaxpoolNMS have limitations, restricting their use to specific stages in certain object detection architectures.

    Purpose of the Study:

    • To develop a generic and parallelizable NMS approach that can replace GreedyNMS in all stages of all object detectors.
    • To enhance the efficiency and accuracy of parallelizable NMS methods.

    Main Methods:

    • Introduced a Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module to improve upon MaxpoolNMS's discretization and local score calculation.
    • Developed PSRR-MaxpoolNMS++ by incorporating Density-based Discretization and Adjacent Scale Pooling for more accurate suppression and efficient duplicate box identification.
    • Extended PSRR-MaxpoolNMS to PSRR-MaxpoolNMS++ for enhanced performance.

    Main Results:

    • PSRR-MaxpoolNMS and PSRR-MaxpoolNMS++ significantly outperform MaxpoolNMS.
    • PSRR-MaxpoolNMS++ achieves competitive accuracy and superior efficiency compared to the standard GreedyNMS.
    • The proposed methods demonstrate effectiveness across all stages of object detection.

    Conclusions:

    • PSRR-MaxpoolNMS and PSRR-MaxpoolNMS++ offer viable, parallelizable alternatives to GreedyNMS.
    • PSRR-MaxpoolNMS++ presents a highly efficient and accurate NMS solution suitable for all object detection pipelines.
    • The developed modules enable a generic and scalable NMS replacement.