ArticleSensors (Basel, Switzerland)2024
Image-Based Concrete Crack Detection Method Using the Median Absolute Deviation.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CLGDS: robust bridge crack detection with YOLO enhanced feature fusion and SIoU optimization.Scientific reports · 2026Article
- Gradient transformer Self-Attention U-Net for enhanced crack detection in concrete bridges.Scientific reports · 2025Article
- Pore Structure Influence on Properties of Air-Entrained Concrete.Materials (Basel, Switzerland) · 2025Article
- Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach.Journal of imaging · 2024Article
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Authors and funding
3 authors.
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Abstract
This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method's performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods.
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