Classification of wood defect images using local binary pattern variants

Rahiddin, Rahillda Nadhirah Norizzaty; Hashim, Ummi Rabaah; Ismail, Nor Haslinda; Salahuddin, Lizawati; Choon, Ngo Hea; Zabri, Siti Normi

doi:10.26555/ijain.v6i1.392

Cited by 10 publications

(9 citation statements)

References 22 publications

(23 reference statements)

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“…This indicates that there is a gap between working to generalize and characterize all the types of defects that are frequently found. [14]- [19], [21], [22], [25] Hole [13], [14], [19], [20], [23], [24] Pocket [5], [13], [17], [29] Stain [13], [25], [30] Decay / Rot…”

Section: Methodsmentioning

confidence: 99%

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A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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Section: Methodsmentioning

confidence: 99%

“…[20], [24] Split [5], [13], [20] Wane [5], [13], [20] Generally, there are two types of research problems when dealing with AVI in the wood industry, namely, defect detection and defect identification. Table 2 lists the previous AVI studies related to the detection and identification of timber defects.…”

Section: Methodsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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“…In nine different types of hardwood lumber, the defect detection model consists of board scanning with Microtec Goldeneye 300 multi-sensor-quality scanner, and data were analyzed with the Purdue GradeView algorithm. The use of local binary pattern variants for wood defect image classification was performed by Rahiddin et al (2020). The basic and variants of the LBP feature set are constructed, from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP.…”

Section: Literature Surveymentioning

confidence: 99%

Edge-glued wooden panel defect detection using deep learning

Chen

Pardeshi

et al. 2022

Wood Sci Technol

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The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.

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“…Backpropagation algorithm has been widely applied in the area such as computer science, agricultural, healthcare and engineering [10][11] [12]. It consists of two steps: forward propagation and backward propagation.…”

Section: Backpropagation In Neural Networkmentioning

confidence: 99%

Design of Single and Dual-axis Solar Tracker System using Neural Network

2020

IJATCSE

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Nowadays, solar energy is becoming more popular as it promotes awareness of an eco-friendly lifestyle. However, solar power generation is insufficient as the primary power generation in Malaysia. To overcome this issue, many private companies are devoting themselves to research to increase the amount of electricity produced by solar panels. Research has shown that a surface perpendicular to direct sunlight increases power generation significantly. Therefore, positioning the solar panel perpendicular to the direct sunlight is crucial as there are many factors to be considered. We will study the difference in dimensional movement and the method of tracking sunlight to make solar panels more efficient in power generation. Light can come from omnidirectional, the movement of the robot must have a higher dimension. Finding the highest intensity of sunlight becomes a challenges because sunlight does not constantly stagnate in a particular location. Therefore, a static solar panel is not as useful as a dynamic solar panel. Besides, the positioning of the sun will change accordingly based on the season. Hence, we need a dynamic solar tracker that can changes its solar panel position as the sun rises or falls. A neural network is chosen as the method of positioning the solar panel by moving with single-axis and moving with dual-axis.

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Classification of wood defect images using local binary pattern variants

Cited by 10 publications

References 22 publications

A review of the automated timber defect identification approach

A review of the automated timber defect identification approach

Edge-glued wooden panel defect detection using deep learning

Design of Single and Dual-axis Solar Tracker System using Neural Network

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