Knots timber detection and classification with C-Support Vector Machine

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers

Evaluation of texture feature based on basic local binary pattern for wood defect classification

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects

Identification of wood defect using pattern recognition technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance

Condition assessment of timber utility poles based on a hierarchical data fusion model

© 2016 American Society of Civil Engineers. This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster-Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method

Classification of wood defect images using local binary pattern variants

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects

Identification Of Wood Defect Using Pattern Recognition Technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the Fmeasure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performanc

Melamine Faced Panels Defect Classification beyond the Visible Spectrum

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution

Surface-type classification using RGB-D

This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract color features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new color features to classifying surface-types. In this approach, Red, Green, Blue-Depth (RGB-D) data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) color channel. The diffused reflectance values can be used to train a multiclass support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other color features including RGB and Lcolor spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and Lab features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in overall accuracy over RGB and Lab by 49.24% and 13.66%, respectively. © 2013 IEEE

Acoustic emission monitoring of wood materials and timber structures: A critical review

The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied