Wood defect detection method with PCA feature fusion and compressed sensing

Robust defect detection in 2D images printed on 3D micro‐textured surfaces by multiple paired pixel consistency in orientation codes

“…Numerous researchers carrying out defect detection for surface inspection have commonly examined steel [34], textile [35, 36], and wood [37, 38]. For printing inspections, examinations have been generally performed using paper materials and pharmaceutical capsules [1].…”

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).…”

Data Augmentation and Layered Deformable Mask R-CNN-Based Detection of Wood Defects

“…Kwon et al [9] used Variance-of-Variance (VOV) contours to describe the texture of an object's surface and amplify irregularities in intensity variation, and used random forests to classify surface defects, which can be used for the detection of wood defects. Zhang et al [10] used principal component analysis (PCA) and compressed sensing-based, self-organizing feature map (SOM) neural network to detect wood defects in wood panel images, with the best classification accuracy of 92%. The common feature of these methods is the need to manually design features such as color, shape, and size of wood defects, which are empirically dependent, along with the existence of good performance in small samples and poor performance for large samples.…”