GSPSO-LRF-ELM: Grid Search and Particle Swarm Optimization-Based Local Receptive Field-Enabled Extreme Learning Machine for Surface Defects Detection and Classification on the Magnetic Tiles

Abstract

Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. However, existing work focuses mainly on the detection rather than the classification. In this article, we propose GSPSO-LRF-ELM that is the grid search (GS) and the particle swarm optimization- (PSO-) based local receptive field-enabled extreme learning machine (ELM-LRF) for the detection and classification of the surface defects on the magnetic tiles. In the ELM-LRF classifier, the balance parameter C and the number of feature maps K via the GS algorithm and the initial weight Ainit via the PSO algorithm are optimized to improve the performance of the classifier. The images used in the experiments are from the dataset collected by Institute of Automation, Chinese Academy of Sciences. The experiment results show that the proposed algorithm can achieve 96.36% accuracy of the classification, which has significantly outperformed several state-of-the-art approaches.