Research On Online Fabric Defect Detection System

Fabric defect detection is one of most significant procedure for quality control in textile manufacturing industry. The manual detection is time-consuming, labor-intensive and devoid of consistency and reliability due to many subjective factors. As a analog of human vision, computer vision technology has been applied widely in industrial surface detection with the advances of digital integration and digital image processing techniques. It can be confirmed that the computer vision based detection system has bright perspective of the automation of fabric defection detection.In this dissertation, initially it is pointed out that the overall task of defect detection can not be accomplished by any single method, and the detection system is divided hierarchically into three levels containing defect judgment, defect segmentation and defect classification. The designs of the hardware and software supporting the corresponding level are discussed respectively. It is also analyzed that the inconsistency in the captured images can be attributed to the spatial variation and noise of the illumination source. The online algorithms are proposed to rectify both inconsistencies to improve the image quality. These algorithms are accurate and facile to be implemented without any additional calibration procedure.The Fuzzy Label Co-occurrence Matrix with its features and the distance based outlier detection are proposed as the real-time defect judgment algorithm, which is based on the tonal classes' substitution for gray levels. The texture tone classification, the fitting for membership functions of fuzzy tonal sets and the spacing parameters selection are detailed. The real-time implementation of the algorithm is also discussed. The proposed method has much greater real-time performance than other methods while maintaining high accuracy.By defining a discriminability function and utilizing some images with synthetic defects, a parameter selection method is proposed to adapt to various fabric types when the Gabor filter bank is applied in defect segmentation. It is argued that only the real part of Gabor filter works during segmentation; the radial orientation can be limited to horizontal and vertical direction; the radial frequency and the length of the FIR filter should depend on the basic frequency of the fabric texture. Though the number of the filters decreases to four for better real-time performance, the Gabor filter bank can segment most defects accurately. Moreover, the whole defect segmentation is independent of an offline calibration and insensitive to noise.The blob analysis based method and the Label-Run Co-occurrence Matrix features are applied to attain the overall area of each defect. The defect is therefore classified into three categories with a certain remark based on the geometric features of the blob area or the exceptional run lengths. Compared to methods based on supervised classification, the proposed methods excel in that they don't need any pre-collected defect samples or any offline calibrations.Finally, the ant colony clustering algorithm is introduced into the area of defect detection since the intrinsic parallel distributive computation architecture values in the real-time performance of the system. Inspired by ant colony clustering algorithm, defects are considered to be the gathering of the locally irregular pixels. Three basic elements of ant clustering algorithm are detailed: ants' environment measurement, that is, local irregularity measurement, pheromone release, diffusion and evaporation and the behavior decision of ants are dissertated in details. Further, the ants are considered as fuzzy inference units and the local irregularity features are then transformed to be depicted by some predefined membership functions, and the speed and direction of ants are inferred by fuzzy rules. Finally the defect detection algorithm based on fuzzy ant colony clustering is consequently proposed.