Title:
Lightweight Alternative Machine Learning Model for Automating Concrete Crack Image Classification
Author(s):
Sang Min Lee, Hyeon-Sik Choi, Chanho Kim, and Thomas H.-K. Kang
Publication:
Structural Journal
Volume:
122
Issue:
5
Appears on pages(s):
97-106
Keywords:
concrete crack image; convolutional neural network (CNN); histogram of oriented gradients (HOG); local binary patterns (LBP); random forest (RF)
DOI:
10.14359/51746755
Date:
9/1/2025
Abstract:
In this study, the challenge of automating concrete crack image classification by developing a lightweight machine learning model that balances accuracy with computational efficiency was addressed. Traditional deep learning models, while accurate, suffer from high computational demands, limiting their practicality in on-site applications. This study’s approach used the random forest (RF) classifier combined with histogram of oriented gradients (HOG) and local binary patterns (LBP) for feature extraction, offering a more feasible alternative for real-time structural health monitoring. Comparative analysis with the convolutional neural network (CNN) model highlights this model’s significantly reduced size and inference times, with only a marginal compromise in accuracy. The results demonstrated that the RF models, particularly RF with LBP, are well-suited for integration into resource-constrained environments, paving the way for their deployment in portable, on-site diagnostic systems in civil engineering. This study contributed a novel perspective to the field, emphasizing the importance of efficient machine learning solutions in practical applications of structural health monitoring.
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