Wood Surface Quality Detection and Classification Using Gray Level and Texture Features

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

“…In these schemes, texture features are extracted from the defected image in spatial domain, and then a classifier is employed to distinguish defective areas from the background. The texture features, in this type of defect detection scheme, are extracted from the second-order statistics of Gray Level Co-Occurrence Matrix (GLCM) and found suitability in applications such as wood [51], carpet wear assessment [52], measure of roughness in machine surface [53] and textile fabric [54,55]. Various methods are also reported to decompose an image into its structure part containing large scale variation and texture part containing small-scale variation in pixel intensity.…”

SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network

“…Currently, machine learning methods applied to wood defect detection mainly include techniques like Gray Level Co-occurrence Matrix (GLCM), image segmentation, Support Vector Machines (SVM), and wavelet neural network [8]. These detection algorithms provide an automated approach for wood surface defect detection, effectively avoiding the numerous steps of manual and machine detection [9,10]. However, these algorithms require manual extraction of wood defect features, followed by classification and recognition of defects, which can slow down the actual detection speed.…”

“…Our approach achieved 86.4% in the mean average precision (mAP) metric, surpassing many existing models. In order to detect wood surface quality, Wang proposed a method for detecting wood surface quality by extracting gray-scale histogram statistical features and GLCM texture features of the wood [9]. The proposed method utilized more pixel information compared with the traditional four-angle method, thus achieving a higher classification accuracy than the previous method.…”

“…Related Works 2.1. Methods Based on Traditional Machine Learning Algorithms Wood defect recognition methods based on machine learning typically involve Gray Level Co-occurrence Matrix (GLCM), image segmentation, Support Vector Machine (SVM), and wavelet neural networks.In order to detect wood surface quality, Wang proposed a method for detecting wood surface quality by extracting gray-scale histogram statistical features and GLCM texture features of the wood[9]. The proposed method utilized more pixel information compared with the traditional four-angle method, thus achieving a higher classification accuracy than the previous method.…”