Abstract:
In order to solve the problems of traditional metal surface quality inspection technology, such as the low precision of defect detection, the low recognition rate of defect detection, and the inaccurate classification of defects, a set of metal sheet surface inspection system based on machine vision was established. Based on the partial differential equation, the median filtering algorithm is improved by using the iso-illumination lines of the image, and the image is pre-processed to significantly suppress the image noise. Using the Maximum Inter-Class Variance Algorithm (OTSU) to adaptively determine the double threshold value of the image, the Gaussian filter in Canny algorithm was improved to extract the gray distribution features of the image, so that it was not affected by brightness and contrast. Finally, using SIFT (Scale-invariant feature transform) algorithm to extract defect feature points, a classifier detection method combining BP (Back Propagation) neural network and SVM (Support Vector Machine) is proposed. The defect detection rate is 92. 68%, and the single image detection only takes 49. 8 ms. The defect detection system effectively extracts and identifies the surface defects of the metal sheet, and meets the requirements of online detection of the surface of the metal sheet.