Affiliation:
1. Department of Electronic and Computer Engineering Universidad de Córdoba Córdoba Spain
Abstract
AbstractAutomatic crack classification plays an essential role in road maintenance. Using many features for the classification is inefficient for implementing embedded systems with low computational resources makes it difficult. Therefore, this work proposes a new data dimensionality reduction (DDR) for crack classification algorithms (DDR4CC). DDR4CC reduces the required information about the cracks to only four features. Using these features, the images can be classified into longitudinal, transverse, and alligator cracks or healthy pavement. DDR4CC is compared with eight DDR methods, and the reduced set of features is analyzed using five different classification algorithms. Besides, five different datasets, generated by a combination of several public datasets, are used. We are proposing a simple DDR method with high interpretability of the data, obtaining very fast computation and high accuracy. Experiments show that DDR4CC enhances the results of the classification algorithms, providing almost perfect classifiers with a minimum computation time.
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction
Reference66 articles.
1. PCA-Based algorithm for unsupervised bridge crack detection
2. Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique;Ahmadi A.;Journal of Industrial and Systems Engineering,2018
3. An integrated machine learning model for automatic road crack detection and classification in urban areas
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