Research And Implementation Of A Machine-Vision-based Quality Detection System For Fabric Surfaces

With the advancement of manufacture and techniques, the product quality is more and more important, and the online quality detection system based on machine vision has been an important quality control method. The paper studies the online quality detection theory and algorithm of fabric surface, which is applied in broad face and high-precision,and based on machine vision. Besides, the paper develops a set of fabric surface quality detection system.At first, due to the characteristics of broad face and high-precision in fabric surface quality detection, this paper designs a kind of structure of master-slave distributed on-line machine vision inspection system which uses multi-image processing subsystem to cooperate detection task, ensures the reliability of dealing with mass image rapidly. In addition, this structure uses Gigabit Ethernet to complete the network transmission of image data and controlling command, which ensures the real-time image acquisition, processing, storage and transmission. Aimed at fabric texture characteristics, the paper designs positive side angle variable lighting of high-power LED bar array, which has strong light and symmetrical illumination, far away from fixed angle lighting’s disadvantages of poor adaptability and defect information extraction.In order to filter noises produced in the fabric image acquisition process by sunlight, shooting angle, camera lens pollution, the thesis analyzes principles of choosing filter according to fabric texture characteristics, discusses the spectrum feature extraction method, fuzzy structuring element texture definition, and the analyzing method of fabric texture, studies spectral feature extraction methods based on energy Sector statistics, and then solves problems of dealing with fabric texture. At the same time, for some detail problems of low filtering performance efficiency and difficult feature extraction process of complex one-dimensional signals, the paper designs the optimizing method of filter type operator, and improves operator’s performance efficiency obviously.For texture characteristics of fabric image, the paper designs the high-precision self-adaptive threshold segmentation which is suitable for finding the target area in irregular texture image. At the same time, based on the need of the recognition of the defects characteristics and its region, the paper designs the window line scanning method with rectangular windowsill in a straight line scan and through the calculation of the total area of the spot in the window to determine whether to retain the point of a straight line. The method solves the problem of the loss of the detects information after pre-processing, and on the basis of it, the superiority is demonstrated by two examples.Then, according to the fabric of the common types of defects, the thesis takes the values of Variance- Density, Contrast- Density, Entropy-Density, Frequency-domain as the basic description parameters for characteristics of defects, unites all kinds of defect feature as unified description, designs fabric defect classified method based on characteristic parameter, researches fabric defect classifier based on mended BP neural network, and realizes fabric’s final quality evaluation.Finally, on the basis of methods and theory above-mentioned, the fabric surface quality detection system is designed and developed. The experimental analysis shows that, the highest detect speed of the system is 200 m/min, the highest detection precision is 0.5 mm. The system can realize real-time classification of common 8 types of defects with 90% accuracy rate.