Automated hyperparameter tuning for crack image classification with deep learning

Deep learning methods have relevant applications in crack detection in buildings. However, one of the challenges in this field is the hyperparameter tuning process for convolutional neural networks (CNN). Thus, the objective of this paper is to propose a automated hyperparameter tuning approach for crack image classification. For this, a public dataset with 40,000 images of walls and floors of several buildings was used. The images are divided into two classes: negative (non-crack) and positive (crack). In this aspect, statistical methods are used for hyperparameter tuning, such as analysis of variance, Scott–Knott method and HyperTuningSK algorithm. Moreover, three new automated machine learning algorithms are proposed: AutoHyperTuningSK, AutoHyperTuningSK-test and AutoHyperTu-ningSK-DA. CNN architecture from the literature (MobileNet) and three types of hyperparameters (learning rate, optimizer and data augmentation) are analyzed. In general, the recommended configurations reached the best results in relation to unselected hyperparameters. In this regard, a selected combinations achieved a mean accuracy of around 99%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99\%$$\end{document} (test experiments) in binary crack classification.