Key Techniques For Surface Defects Online Detection Based On Machine Vision

Product surface quality is an important part of the product quality, and it is also an important guarantee for the product commercial value. As an advanced product quality monitoring method, machine vision inspection technology has been paid more and more attentions by manufacturing enterprises. This paper does a systematic study for key technologies of surface defects online detection based on machine vision.Based on the main procedures of surface defects visual detection, which is image acquisition, defects segmentation and defects discrimination, this paper does design and experimental analysis for image processing and key algorithms. Aiming at requirements for online detection, this paper also proposes methods for algorithm efficiency analysis and multithreading scheme for online detection software, establishes a more complete system structure for surface defects visual online detection. Some testing experiments have been made with the steel plate surface defects detection as the applying practice.1. Aiming at the acquisition of high quality images, proposes the lighting scheme design principle that based on the characteristics of measured object. Taking surface pit defect detection for instance, dose mathematical modeling for pit defect detection based on linear CCD system, and presents the image characteristic for pit defect, and does mathematical modeling for the depth field of surface defects detection imaging system.2. Studies for defects segmentation with different mode and different background; Designs completely algorithm flow chart for defects segmentation based on edge feature. Proposes a set of anti-noise edge detection algorithm based on the correlation feature of wavelet transform coefficients of different levels. Makes a discussion and research for rational coefficients wavelet filter design, gives a length 8-4 rational symmetric compactly-supported biorthogonal wavelet filter, and does experiments for the comparison of different wavelet filters application results.3. Studies for the extraction of image characteristic parameters in space domain, projection domain and wavelet transform domain, and does dimension reduction by the method of Principal Component Analysis. Designs the decision tree for defects classification based on DAGSVM algorithm, and adopts hierarchical cluster method to optimize the decision tree design. 4. Using prior analysis and afterwards testing methods, analyses the time efficiency for the key algorithms proposed by this paper. With the techniques of real-time acquisition, quasi real-time processing and multithread programming, gives the software structure for surface defects online detection, and designs the storing file system based on memory mapping file technique.5. Studies for the application of steel surface defects online detection. Makes system structure design according to measurement indicators, and does testing experiments to verify the algorithms for defect segmentation, feature extraction, and pattern classification. Establish the roller experiment prototype in the laboratory environment, which provides experimental conditions for high speed online surface defect detection.