Title:
Lightweight Alternative Machine Learning Model for Automating Concrete Crack Image Classification (Prepublished)
Author(s):
Sang Min Lee, Hyeon-Sik Choi, Chanho Kim, and Thomas H.-K. Kang
Publication:
Structural Journal
Volume:
Issue:
Appears on pages(s):
Keywords:
concrete crack image; convolutional neural network; histogram of oriented gradients; local binary patterns; random forest
DOI:
10.14359/51746755
Date:
4/9/2025
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 a 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 our 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.