Automated defect inspection and classification of leather fabric

Automated fabric defect detection—A review

“…The techniques used in the morphological approach (MA) are basically nonlinear. Six examples of the MA for fabric inspection are reviewed namely (a) [15], (b) [57], (c) [58], (d) [45], (e) [59] and (f) [60,61]. Briefly, the detection success rates of (a)-(f), except for (b) and (c), ranged from 54.13% [45] to 97.4% [60].…”

“…Although it seemed reliable, no discussion was given for fabrics of other wallpaper groups. Kwak et al [59] believed that the illumination and the distance between camera and fabric (leather) were two determining factors, in that the distance could be adequately adjusted by trial and error.…”

Motif-based defect detection for patterned fabric

“…Adopting the aforementioned classification style, it becomes clear that many of the defect detection methods reported are for the p1 pattern [6,9,10,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Straightly speaking, p1 defines a homogeneous texture, with just one lattice repeating itself over the entire image.…”

“…As modern manufacturing processes [7] of these patterned objects are not perfect, defective patterns are frequently found on these items. In order to control quality [6,[8][9][10], manual inspection is often carried out although it is labor intensive. On the other hand, machine inspection can potentially offer efficiency and minimize labor cost, but hinges on whether the automated method concerned is able to detect these defects as well as human beings.…”

A simple computational framework for defect detection system with orthogonal polynomials transcoded coefficients

“…Hence automatic defect identification, with the help of digital image proceesing scheme, is a natural alternative. In this direction, defect detection in application areas such as automated manufacturing [2,3], textile fabric [4][5][6][7][8][9][10][11], film industry [12][13][14], wood [15][16][17], construction industry [18], Printed Circuit Board (PCB) ( [19][20][21], wafer [22][23][24][25], solar cells [25][26][27][28], paper industry [29], leather [30], food processing [31,32], and rails [33] are reported in the literature. These image analysis techniques, designed for defect detection, are implemented either on non-textured surface like paper and glass materials or homogeneously textured surfaces like textile or on structural patterns like semiconductor wafer dies and Liquid Crystal Display (LCD).…”