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Improved NMS Filter of Similar Categories for Road Damage Detection

Published: 25 May 2020 Publication History

Abstract

Road damage detection aims to detect and classify road damage on images taken by car smartphones. In the task, Faster R-CNN achieves the best results. However, Faster R-CNN neglects the existence of relevance for similar categories. For the reason above, we propose the IouNmsFilter (INF), an improved NMS and filter module based on IoU of candidate bounding boxes to acquire rich IoU information between similar road damage categories. In the INF, we propose Rough Filter (RF) and Fine Filter (FF) to refilter candidate boxes in a serial manner. RF guarantees that each category retains at least one candidate box after removing the boxes whose scores are lower than the threshold. Based on RF, FF clusters the boxes into different groups according to the IoU information and retains the box with the highest score in each filtered group. As a result, the candidate boxes discarded by NmsFilter(NF) of Faster R-CNN can be recycled to improve the recall metric. The proposed method remarkably advances the state-of-the-art approaches.

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    cover image ACM Other conferences
    ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing
    August 2019
    584 pages
    ISBN:9781450376259
    DOI:10.1145/3387168
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    Published: 25 May 2020

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    Author Tags

    1. High similarity between categories
    2. IouNmsFilter
    3. Road damage detection

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    ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
    Overall Acceptance Rate 186 of 424 submissions, 44%

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