Crack Image Classification: Performance Comparison Among Different Machine Learning Algorithms

Classification of surface crack images is an ever-evolving area of research that aims to develop machine learning models capable of identifying and categorizing cracks in materials. This study concentrates on evaluating and comparing the effectiveness of various machine learning algorithms when empl...

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Bibliographic Details
Published in2024 8th International Conference on Image and Signal Processing and their Applications (ISPA) pp. 1 - 6
Main Authors Okba, Benelmir, Fathi, Dhiabi
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2024
Subjects
Classification algorithms
Data mining
deep learning
image classification
machine learning
Machine learning algorithms
Orange data mining
Prediction algorithms
Signal processing algorithms
Support vector machines
surface cracks
Vectors
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Summary:Classification of surface crack images is an ever-evolving area of research that aims to develop machine learning models capable of identifying and categorizing cracks in materials. This study concentrates on evaluating and comparing the effectiveness of various machine learning algorithms when employed in the classification of crack images. The objective is to identify the most suitable classification technique for this purpose. To conduct this comparative study, several machine learning algorithms are selected, such as k-nearest neighbors (KNN), decision trees, support vector machines (SVM), random forests and neural networks (NN). Each of these algorithms is trained and evaluated on a dataset of previously annotated crack images.
DOI:10.1109/ISPA59904.2024.10536827