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Review on computer vision-based crack detection and quantification methodologies for civil structures
Ist Teil von
Construction & building materials, 2022-11, Vol.356, p.129238, Article 129238
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
•Image-based approaches for crack analysis in civil structures are reviewed.•Learning-based methods for qualitative crack detection are systematically evaluated.•Crack classification, region localisation, and attention detection are higlighted.•Supervised and unsupervised crack detection methods are reviewed.•Pixel-level segmentation methods for crack quantitative evaluation are presented.
Computer vision-based crack analysis for civil infrastructure has become popular to automatically process inspection imaging data for crack detection, localisation and quantification. Some literature reviews have been conducted, which mostly focus on qualitative damage evaluation or damage segmentation, missing the methodology categorisation for applicability-oriented quantitative crack assessment. To fill the gap, this review provides a comprehensive overview of state-of-the-art image-based crack analysis under various conditions in both qualitative and quantitative aspects, particularly focusing on image processing and deep learning-based methodologies from image-level detection to pixel-level segmentation and quantification. The key challenges and research gaps are also discussed as follows, which indicate the importance of future research: (1) developing data model methodologies to resolve the difficulties due to the image data deficiency; (2) building a learning-based model capable of processing data with complex backgrounds; (3) enhancing the scene generalisation on different detection tasks; (4) establishing a lightweight mechanism for real-time crack analysis; (5) constructing learning-based systems that comprehend the local and global contexts during crack evaluation; (6) developing a semi-supervised mechanism for more information capturing and (7) establishing attention-based models for enhanced segmentation performance.