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.
Highlights
(e) neural network (MG-convolutional neural network (CNN)). e framework has better classification performance, but there is the problem that the classification results are unstable in early training
Experiment Settings. e dataset used in the experiment was from the dataset on surface defect detection of the magnetic tile collected by Institute of Automation, Chinese Academy of Sciences [2]. e folder name of the dataset is magnetic-tile-defect-datasets
In the extreme learning machine (ELM)-LRF algorithm, in order to analyze the influence of the balance parameter C and the number of feature maps K on the algorithm, the grid search (GS) is used to optimize the two parameters. e parameter optimization is divided into two parts: rough optimization and fine optimization
Summary
(e) neural network (MG-CNN). e framework has better classification performance, but there is the problem that the classification results are unstable in early training. We use the GS method to obtain the optimal parameter combination (C, K) more accurately and divide the GS method into two parts: rough optimization and fine optimization; PSO algorithm is proposed to optimize the initial weight Ainit of ELM-LRF and further classify the defect categories. (1) Because ELM-LRF has poor initial weights stability, we use the particle swarm optimization (PSO) to optimize the initial weights of ELM-LRF, which improves the classification accuracy of the classifier (2) In order to improve the performance of the classifier, the method of grid search (GS) rough optimization and fine optimization is used to optimize the balance parameter C and the number of feature maps K in ELM-LRF (3) Using the optimized ELM-LRF to classify the surface defect categories in the detected images and compared with some advanced multicategory classification algorithms, our proposed method has higher classification accuracy e remainder of this article is organized as follows.
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