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 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.