A robust self-supervised approach for fine-grained crack detection in concrete structures.

Sohaib, Muhammad; Hasan, Md Junayed; Shah, Mohd Asif; Zheng, Zhonglong

Abstract

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Citation

SOHAIB, M., HASAN, M.J., SHAH, M.A. and ZHENG, Z. 2024. A robust self-supervised approach for fine-grained crack detection in concrete structures. Scientific reports [online], 14(1), article number 12646. Available from: https://doi.org/10.1038/s41598-024-63575-x

• Journal Article Type: Article
• Acceptance Date: May 30, 2024
• Online Publication Date: Jun 2, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jun 11, 2024
• Publicly Available Date: Jun 11, 2024
• Journal: Scientific reports
• Electronic ISSN: 2045-2322
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: 12646
• DOI: https://doi.org/10.1038/s41598-024-63575-x
• Keywords: Self-supervised YOLO; Concrete cracks detection; Gaussian adaptive weights; Pseudo-labeling; Curriculum learning; Structural health monitoring
• Public URL: https://rgu-repository.worktribe.com/output/2372256