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CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Madras, India

    Shreyas Kulkarni, Shreyas Singh, Dhananjay Balakrishnan & Siddharth Sharma

  2. University of Illinois Urbana-Champaign, Champaign, USA

    Saipraneeth Devunuri

  3. Rizzo International, Inc., Pittsburgh, USA

    Sai Chowdeswara Rao Korlapati

Authors
  1. Shreyas Kulkarni
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  2. Shreyas Singh
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  3. Dhananjay Balakrishnan
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  4. Siddharth Sharma
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  5. Saipraneeth Devunuri
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  6. Sai Chowdeswara Rao Korlapati
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Corresponding author

Correspondence to Shreyas Kulkarni .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Kulkarni, S., Singh, S., Balakrishnan, D., Sharma, S., Devunuri, S., Korlapati, S.C.R. (2023). CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_12

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