Developing a new deep learning CNN model to detect and classify highway cracks. Issue 4 (16th August 2021)

• Record Type: Journal Article
• Authors: Elghaish, Faris; Talebi, Saeed; Abdellatef, Essam; Matarneh, Sandra T.; Hosseini, M. Reza; Wu, Song; Mayouf, Mohammad; Hajirasouli, Aso; Nguyen, The-Quan
• Is Part Of: Journal of engineering, design and technology. Volume 20:Issue 4(2022)
• Page Start: 993
• Page End: 1014
• Publication Date: 2021-08-16
• Subjects: Deep learning -- Classify -- Highway cracks -- Optimization algorithms -- Convolutional neural network (CNN); Engineering -- Periodicals; Engineering design -- Periodicals; Industrial design -- Periodicals; Technology -- Periodicals; 620.005
• DOI: 10.1108/JEDT-04-2021-0192
• Languages: English
• ISSNs: 1726-0531

Abstract

Abstract : Purpose: This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates. Design/methodology/approach: A sample of 4, 663 images of highway cracks were collected and classified into three categories of cracks, namely, "vertical cracks, " "horizontal and vertical cracks" and "diagonal cracks, " subsequently, using "Matlab" to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates. Findings: The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam's optimization algorithm. Practical implications: The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches. Originality/value: A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.