Convolution neural network model for an intelligent solution for crack detection in pavement images
by Aaron Rasheed Rababaah; James Wolfer
International Journal of Computer Applications in Technology (IJCAT), Vol. 68, No. 4, 2022

Abstract: This paper presents a deep learning solution using convolution neural networks for pavement crack detection. The advancements in machine learning and machine vision open new opportunities for researchers to explore the power of deep learning instead of classical machine learning to solve old and new problems. We propose a convolutional neural network model to detect cracks in pavement. Our solution is based on a multi-layer model that encompasses a raw image input layer, convolutional layers, activation layers, max-pooling layers, a flattening layer and multi-perceptron neural network as classification layers. MATLAB was our development platform to create and test the solution. A total of 500 sample images were collected from publicly-available sources. Sixteen different experiments were conducted to determine the best configuration for the proposed model in terms of the number of features. The results of the experiments suggest that the proposed model is effective with a detection accuracy of 96.6% when correctly configured.

Online publication date: Thu, 01-Sep-2022

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