Digital Characterization Of Garment Defects Detection

China is the largest garment exporter in the world. Quality control and inspection of garment production are very important. Among them, clothing defect detection is the most important part. Artificial defect detection of clothing can cause not only intensive laboring, but also heavy psychological burden, low efficiency, and big error. Due to above facts, automatic detection of defects by means of image processing, analysis techniques and modern mathematics becomes a hot research topic of international attention in recent years. In this thesis, design of a visual, online detection system used in the identification of clothing production defects is proposed. Currently, most online detection for clothing defects is still in the exploratory stage, far from application. Solving the problem of automatic identification of clothing defects has academic and practical significance. The main work and contributions of this thesis are as following:(1) Dual image fusion method simulationThis thesis also gives image fusion program of wavelet multi-scale decomposition and presents design of picture mixed multi-resolution analysis. For different multi-scale geometric analysis with complementary features, different image transformation methods of complementary characteristics were combined to obtain mixed multi-resolution image. We constructed a mixed multi-resolution image fusion analysis framework. In mixed multi-resolution decomposition domain, we fused decomposition coefficients. And then the fusion image is obtained by inverse transform. Combining complementary nature of wavelet transform and Curvelet transform, we constructed a mixed wavelet and Curvelet transform. Fusion image of traditional multi-focus image fusion tends to partially lose clarity of the source images. However, in this paper, through simulating handwork of cut-paste method, a framework of split-merge combination with features of integration of multi-focus image area was built. We also proposed a design of image segmentation on the basis of clarity of images measured, and achieved regional multi-focus vision fusion. According to the standards of image spatial frequency and morphological wavelet transform coefficients, regional image algorithm was design. In the basis of the pixel neighborhood spatial frequency domain as its sharpness measurement, and with the combination of morphological operators, division of clear regional and fuzzy area was directly obtained. And by the multi-focus image that was taken in two different angles and had two printing defects as object image, the actual effect of the double image fusion method was verified.(2) Clothing defect image preprocessing SimulationFor simple texture defects (i.e., textured gray value is equal to or below70%of garment defects value) images, pretreatment process typically includes noise canceling, image enhancement, and so on. In order to highlight image structure feature of defect region, we used binary image processing to separate defect area and background. Taking simple texture sutures as an example, we described simple texture defects image preprocessing in details. Image processing results showed very clearly that the suture stitches were extracted, the defects were completely reserved on line track. For complex texture defects (textured gray value is between70%and85%, including85%) images, pretreatment process is also composed of noise canceling, image enhancement, and so on. Edge detection is the most important image processing method of this type for defect image. Based on complex texture sutures, complex texture defects image preprocessing was described in detail. Image processing results show that although extracted sutures trace is not as clearly as the original images, general defect characteristics are still completely reserved on line track. For super complex texture defects (textured gray value is higher than85%of garment defects value) images, pretreatment process comprises noise canceling, image enhancement, and so on. In order to highlight image defect region, it is needed to enhance local defect image by warps defects and zonal defects performance on gray images. For super complex texture defects, taking sewing material folds as an example, we described in detail the super complex texture defects image preprocessing. Image processing results show that although extracted defect characteristics was no as clearly as the original images, general defect characteristics was relatively complete reserved on line track.(3) Clothing defect image feature extraction simulation In order to use feature quantity as little as possible to achieve the best classification ability, we study the correlation of eight texture features. The smaller the correlation coefficients of two features, the less similar the two features, and the greater difference the information in contained content shows. For images, the less similarity means the smaller redundancy of image information. In the image classification process, we often choose small image information redundancy in classification in order to facilitate image classification, which can reduce the amount of computation. We selected six smaller correlation characteristics coefficient to describe areas, including mean, standard deviation, smoothness, third moment, consistency and entropy, and used these texture features as the ultimate types of texture features. After image processing, taking the simple suture defect images, complex texture defect images, super complex texture defect image as objects, we calculated and analyzed these six texture features of the object. We compared the value of extracted parameters and found that the mean, standard deviation, third moment was significantly different from that of the standard stitches of simple suture defect images(for example, heavy line defects), the mean, standard deviation, third moment is significantly different from that of the standard stitches of complex texture defect images (for example, heavy line defects), and the mean, standard deviation, third moment is significantly different from that of the standard stitches of super complex texture defect images (for example, sewing material folds defects). These parameters can be used to identify clothing defect classification. Inputting them into the neural network for training, we can get information concerning these types of defect identification and classification.(4) Clothing defect image pattern classification SimulationIn this thesis, we use BP and radial basis neural network to constitute clothing defect images classifier, especially focusing on the BP neural network and its improvement. From the classification results of2,000garment samples after dual (multiple) image fusion algorithm combined morphological operators and spatial frequency of simple texture defects(normal lines, break lines, heavy lines, jumpers lines, plain weave fabric, plain weave fabric defects, watermark defects, dyeing defects, each250parts), complex texture defects(standard lines, heavy lines, knitted fabrics, knitted fabric defects, woven fabrics, woven fabrics defects, woven fabrics, woven fabrics defects, each250parts),ultra-complicated texture defect (Standard stitch sewing materials, sewing material folds defects, warp defect, floating through the defect, hanging by the defect, zonal defects, weft defects, hole defects each250parts), we can see the classical BP algorithm can not realize classification of clothing defect image. In the course of this investigation, BP network has been improved, and introduction of momentum and adaptive lrBP gradient descend training function and Levenberg-Marquardt optimization algorithm has been performed. Momentum method can suppress network trapped in local minima. LM optimization algorithm has fewer iterations, faster convergence, and higher precision than the BP algorithm ("trainlm" The training steps take as long as19steps). From the experimental results, the training function "traingdx" has the correct rate of75percent and the training function "trainlm" has correct rate of92%. These experimental results are satisfactory. From the classification results of2,000garment samples after dual (multiple) image fusion algorithm combined morphological operators and spatial frequency of simple texture defects, complex texture defects, super complex texture defects, respectively, we can see Radial Basis Function network algorithm can achieve clothing defect classification. From the experimental results, Radial Basis Function network algorithm has the correct rate of85percent. These experimental results are satisfactory, too. It means that in addition to BP network which can realize clothing defect image pattern recognition, the other networks are also realizing clothing defect image pattern recognition, but there is a problem of optimum.(5) Binocular image system designThis paper illustrates a basic calculation process for acquiring the freedom of binocular image system and baseline selection. The key issues of matrix visual platform achieved for mechanical drive control are introduced in details. Main body of binocular image system and hardware structure were designed, with two cameras simulating the human eyes, and through their cross components up-down and left-right rotation was achieved. Main body of binocular image acquisition system was designed to achieve horizontal saccades, Span, tilt, and camera steering, among which rapid, dynamic interactive adjustment was achieved. The camera rotation was driven by the motor. Four DC direct drive motors were adopted to realize the control of four freedoms. SCM (Single Chip Microcomputer) control system, a89C51microcontroller, was used as the main CPU. And software flow chart was given. Target feedback speed was adjusted and controlled through SCM PID control algorithm, with complete and mature implementation and accuracy. It needs not very complicated steps and harsh conditions to achieve the defect detection in clothing industry.Objective, accurate automatic defect detection in garment production makes garment production to achieve high-quality automated quality control and possible detecting control, which is the main motivation for this paper. In this paper, solutions of defects visual detection system for garment production in all aspects are proposed and a new thinking for the clothing defects online measurement and detection is offered. This provides certain references for the future research and development of similar systems.