Fabric inspection based on the Elo rating method

Abstract

Automated visual inspection of patterned fabrics, rather than of plain and twill fabrics, has been increasingly focused on by our peers. The aim of this inspection is to detect, identify and locate any defects on a patterned fabric surface to maintain high quality control in manufacturing. This paper presents a novel Elo rating (ER) method to achieve defect detection in the spirit of sportsmanship, i.e., fair matches between partitions on an image. An image can be divided into partitions of standard size. With a start-up reference point, matches between various partitions are updated through an Elo point matrix. A partition with a light defect is regarded as a strong player who will always win, a defect-free partition is an average player with a tied result, and a partition with a dark defect is a weak player who will always lose.After finishing all matches, partitions with light defects accumulate high Elo points and partitions with dark defects accumulate low Elo points. Any partition with defects will be shown in the resultant thresholded image: a white resultant image corresponds to a light defect and a grey resultant image corresponds to a dark defect. The ER method was evaluated on databases of dot-patterned fabrics (110 defect-free and 120 defective images), star-patterned fabrics (30 defect-free and 26 defective images) and box-patterned fabrics (25 defect-free and 25 defective images). By comparing the resultant and ground-truth images, an overall detection success rate of 97.07% was achieved, which is comparable to the state-of-the-art methods.

Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

“…Traditional methods cannot efficiently recognize the normal pattern; furthermore, they fail to localize the defect region. Some methods devised for pattern fabric defect detection, such as the ELO rating (ER) method [31], and wavelet-preprocessing golden image subtraction (WGIS) [21], et al,which are performed in a supervised approach, require non-defective samples; moreover, the detection accuracy of these methods depends on precise alignment and choosing a suitable template.…”

Fabric Defect Detection Based on Pattern Template Correction

“…Patterned fabrics are in periodic variations with complicated pattern features. To deal with this kind of fabrics, many methods such as WGIS [23], BB [24], RB [25], ID [26], and ER [27] are proposed. For unpatterned fabrics, the key problem is to reduce the influence of texture features.…”

Fault detection of fabrics using image processing methods

“…Their detection rate for 336 patterned images has been found about 97%. Their work has so effective on different kinds of defects [15]. Hu has proposed a novel method which is based on genetic elliptical gabor filter.…”