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Review article: Automated fabric defect detection-A review

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Fabric defect detection using morphological filters

This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme.

An automated inspection system for textile fabrics based on Gabor filters

Biogeochemical evidence of Holocene East Asian summer and winter monsoon variability from a tropical maar lake in southern China

This paper proposes a real-time computer vision system for detecting defects in textile fabrics. The developments of both the hardware and software platforms are presented. The design of the prototyped defect detection system ensures that the fabric moves smoothly and evenly so that high quality images can be captured. The paper also proposes a new filter selection method to detect fabric defects, which can automatically tune the Gabor functions to match with the texture information. The filter selection method is further developed into a new defect segmentation algorithm. The scheme is tested both on-line and off-line by using a variety of homogeneous textile images with different defects. The results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed system

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A real-time computer vision system for detecting defects in textile fabrics

This paper presents a methodology to solve the manufacturing cell creation and the production scheduling problems for designing virtual cellular manufacturing systems (VCMSs). The objective is to minimize the total materials and components travelling distance incurred. The methodology consists of (i) a mathematical model that describes the characteristics of a VCMS and includes constraints such as delivery due dates of products, maximum capacities of resources, critical tools limitation, and (ii) an ant colony optimization (ACO) algorithm for manufacturing cell formation and production scheduling. Since the proposed ACO algorithm does not indicate a feasible schedule explicitly, two simple heuristics are developed to assign workstations to the operations of the jobs, and to construct the final schedule. To demonstrate the effectiveness of the proposed methodology, both the ACO algorithm and the genetic algorithm are applied to design manufacturing cells for a company in China producing internal combustion engine components. Comparison of the results obtained with the results supplied by the company on the existing manufacturing system show that both the ACO algorithm and the genetic algorithm together with the virtual cellular manufacturing concept perform better than the current manufacturing practice in terms of average workstation utilization, product completion time and system throughput. Also, the results of a set of randomly generated numerical experiments show that the proposed ACO algorithm generates excellent final solutions in a much shorter computation time when compared with the genetic algorithm. Therefore, the mathematical model and the ACO algorithm proposed in this paper form a simple, but effective and efficient methodology to solve the manufacturing cell creation and production scheduling problems for designing VCMSs.

P. Peng

Papers

Fabric defect detection using morphological filters

An automated inspection system for textile fabrics based on Gabor filters

Biogeochemical evidence of Holocene East Asian summer and winter monsoon variability from a tropical maar lake in southern China

A real-time computer vision system for detecting defects in textile fabrics

An ant colony optimization algorithm for scheduling virtual cellular manufacturing systems