Classification Method for Defect Image Based on Feature Extraction

Being aimed at the characteristic in complexity and levity of defect image surface, a novel method combined NMI feature with invariant feature in time domain to conceive the statistic feature of defect images is put forward.Simultaneously, compactness feature, L-S factor feature and linearity feature in the rectangular region are developed as one basis of defect classification.Moreover, in frequency domain, a method which can extract features in the rectangular region of central bright area of defect spectrum image is proposed, and maximum difference and average difference of gray value of all the pixels in this rectangular region are developed as another important basis of defect classification.This paper also applies BP neural network to the automatic classification of defect images, constructs the defect classifier and tests six types of common defects collected from online data.The experimental result shows that the new features extraction method increases the validity of classification of feature greatly and this BP classifier has high identification accuracy and the overall recognition rate is over 90%.This new technique resolves the difficulty of defect classification on defect images to some extent.