ACRM: Attention cascade R-CNN with mix-NMS for metallic surface defect detection

Junting Fang, Xiaoyang Tan, Yuhui Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Scopus citations

    Abstract

    Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

    Original languageEnglish (US)
    Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages423-430
    Number of pages8
    ISBN (Electronic)9781728188089
    DOIs
    StatePublished - 2020
    Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
    Duration: Jan 10 2021Jan 15 2021

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651

    Conference

    Conference25th International Conference on Pattern Recognition, ICPR 2020
    Country/TerritoryItaly
    CityVirtual, Milan
    Period01/10/2101/15/21

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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