A Method of Metal Button Defect Detection Based on Extreme Learning Machine and Sparse Representation

We studied the algorithms of button defect detection, while metal button defect detection is seldom explored in the literature. In this paper, we propose to effectively improve the detecting speed, accuracy, and robustness of the model. We propose a new method of cascading Extreme Learning Machine (ELM) and Sparse Representation Classification (SRC). This method transforms the defect detection problem into a pattern classification problem. First, we preprocess the input button images via eliminating reflection, edges extraction, and dimensionality reduction. ELM has a faster learning speed and better detecting accuracy than the single layer perceptron and support layer machine. ELM was utilized to estimate the probability of the defective buttons, with parameters obtained from the five-fold cross-validation method. SRC was used to reclassify those button images with high noise. Our method improved the detection accuracy, as well as guaranteed the detecting speed. We showed state-of-the-art performance in comparison to other approaches.