Efficient data dimensionality reduction method for improving road crack classification algorithms
Author
Rodríguez Lozano, Francisco J.
Gámez Granados, Juan Carlos
Palomares Muñoz, José Manuel
Olivares Bueno, Joaquín
Publisher
WileyDate
2023Subject
Data compressionData analysis
Classification models
Civil infrastructures
Pavements
Datasets
Crack classification algorithms
METS:
Mostrar el registro METSPREMIS:
Mostrar el registro PREMISMetadata
Show full item recordAbstract
Automatic 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.