Skip to main content

Research Repository

Advanced Search

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

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


Muhammad Sohaib

Mohd Asif Shah

Zhonglong Zheng


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.


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:

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
Keywords Self-supervised YOLO; Concrete cracks detection; Gaussian adaptive weights; Pseudo-labeling; Curriculum learning; Structural health monitoring
Public URL


You might also like

Downloadable Citations