Study On Key Technology Of Automatic Online Image Inspection For Oil-seal Surface Defects

With the continuous improvement of the degree of industrial productionautomation, how to achieve the real-time online testing in the process of multi-species,mass production has long been the problem in industrial fields and the goal pursuedby production enterprises. Reference to product quality standards and combining withthe actual production situation of enterprises, this project is developed to detectsurface defects for oil seal based on image inspection technique. In view of thespecific requirements of oil seal online production and testing, a computer visioninspection system is set up, the theory and method of image detection technology foroil seal surface defects is discussed detailly, including ROI segmentation, imagemosaic, defect edge detection, defects classification and identification, etc. The mainwork included in the dissertation is shown as follows:1) According to oil seal surface quality standards and production requirements,the performance of oil seal defects testing system is quantified concretely. An onlineimage inspection system is designed and built, mainly including the image acquisitionsystem baed on telecentric lens and industrial CCD camera, the low angle ring LEDlighting system, and the oil seal clamp rotation mechanism. This scheme can achieveoil seal circumference equal portions image acquisition, fit the testing requirements ofhigh-speed, wide size range and high resolution, and optimize configuration of fieldof view, resolution and hardware.2) With a view to the characteristics of oil seal image decile acquisition process,a simple method is taken to realize oil seal sub-images mosaic with priori knowledgeand rigid transformation. Taking into account the difference of the qualityrequirements of oil seal different ring regions, that is the same type of defect indifferent region being quantified differently, oil seal ROI segmentation can solve theproblem and simply the following image processing.3) Considering the particularity of oil seal surface defects and systemspecifications, a few different approaches to detect oil seal defects’ edge are putforward in detail and their effects comparison are analyzed. An improved thresholdwavelet local modulus maxima edge detection method is presented owing to thetime-frequency performance of wavelet transform. Meanwhile because of color imagecontaining more edge information, the oil seal surface defects detection methods based on different color space are advanced. Different oil seal defects’ edge detectionapproaches have their own particularity.4) In connect with the speciality of various surface defects of oil seal, thedissertation not only defines the feature parameter space of defects’ type, but alsotakes advantage of the principal component analysis method to extract effectivefeatures for oil seal defects classification. Meanwhile the project explores the supportvector machine classification and recognition method detailedly, builds classifiers foroil seal upper end area and lip area respectively, and fufills oil seal surface defectsclassification and identification.