Surface Defect Target Identification on Copper Strip Based on Adaptive Genetic Algorithm and Feature Saliency

To enhance the stability and robustness of visual inspection system (VIS), a new surface defect target identification method for copper strip based on adaptive genetic algorithm (AGA) and feature saliency is proposed. First, the study uses gray level cooccurrence matrix (GLCM) and HU invariant moments for feature extraction. Then, adaptive genetic algorithm, which is used for feature selection, is evaluated and discussed. In AGA, total error rates and false alarm rates are integrated to calculate the fitness value, and the probability of crossover and mutation is adjusted dynamically according to the fitness value. At last, the selected features are optimized in accordance with feature saliency and are inputted into a support vector machine (SVM). Furthermore, for comparison, we conduct experiments using the selected optimal feature subsequence (OFS) and the total feature sequence (TFS) separately. The experimental results demonstrate that the proposed method can guarantee the correct rates of classification and can lower the false alarm rates.