Development of a field-applicable Neural Network classifier for the classification of surface defects of cold rolled steel strips

냉연강판의 표면결함 분류를 위한 현장 적용용 신경망 분류기 개발

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.