Oil-seal surface defect automatic detection and recognition method based on image processing

A method for detecting and recognizing oil-seal surface defects based on image processing was put forward. According to the requirement of qualitative judging accuracy index based on quantitative oil-seal surface quality, an oil-seal surface defect online visual detection system was designed and built combining with production practice. The system adopts servo motor synchrodrived rotation mechanism to acquire the image of oil-seal aliquot circumference, and the aliquot circumference image is partitioned into different detection regions with image preprocessing algorithm. The wavelet transform modulus maxima edge detection algorithm is used to realize oil-seal defect detection; then the multiple feature values describing oil-seal defects are extracted, principal component is selected and the dimension of the defect features is reduced. Support vector machine M-ary classification strategy is used to classify and recognize the oil-seal defects. The experiment results show that the proposed system and method are feasible and have practical populization value.