Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards

Cao, Jun; Liang, Hao; Lin, Xue; Tu, Wenjun; Zhang, Yizhuo

doi:10.15376/biores.12.1.19-28

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Classifier Reference Eager learners Decision tree [17], [59], [72] Random forest [32], [72], [74] Naïve Bayes [59], [71], [75], [76] SVM [16], [17], [24], [38], [45], [76]- [78] Neural network [8], [14]- [16], [29], [40], [46], [47], [49], [59], [75]- [77], [79]- [83] Lazy learner others k-NN [17], [27], [59], [72], [78] Particle swarm optimization [17], [27], [59], [72], [78] Genetic algorithms [73] Bees algorithms [84]…”

Section: Classification Methodsmentioning

confidence: 99%

“…Nevertheless, some research that used a particle swarm optimizationbased lazy learning particle classifier yielded promising results [33], [73]. However, exceptions were made for two machine learning research that accomplished competitive results by using near-infrared (NIR) [16], [38]. The research, however, could not be equitably analyzed due to the implementation of different technologies.…”

Section: Machine Learning In the Identification Of Timber Defectsmentioning

confidence: 99%

“…Nonetheless, a comparison of the CNN model to the SIFT+k-NN model [27] demonstrated the superiority of deep learning, with the CNN model achieving an accuracy of 88.09% for the identification of knots in contrast to its counterpart. [40], [41], [94] AlexNet [30], [41], [93], [90] VGG (16,19) [25], [30], [40], [93] ResNet (18,34,50,152) [12], [26], [30], [42], [90], [93] Other custom classifiers [22]- [25], [27], [28], [95] 4. DISCUSSSION This paper reviewed previous works on the automated inspection of timber surface defects, including various kinds of approaches targeting both the detection and identification of timber defects.…”

Section: Deep Learning In the Identification Of Timber Defectsmentioning

confidence: 99%

See 2 more Smart Citations

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: Classification Methodsmentioning

confidence: 99%

Section: Machine Learning In the Identification Of Timber Defectsmentioning

confidence: 99%

Section: Deep Learning In the Identification Of Timber Defectsmentioning

confidence: 99%

See 1 more Smart Citation

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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“…In recent years, various methods based on machine vision and computer science have been developed to detect the quality of wood. Non-destructive wood testing methods that are now commonly used include near-infrared spectroscopy testing [ 4 , 5 , 6 ], ultrasonic testing [ 7 , 8 , 9 ], X-ray testing [ 10 , 11 ], laser testing [ 12 , 13 ], and acoustic emission technology [ 14 , 15 , 16 ]. Good results have been obtained by combining the above methods of extracting the surface or internal features of wood with classic machine learning methods, such as back propagation neural network (BP), support vector machine (SVM), and K-means clustering algorithm to predict and classify wood features.…”

Section: Introductionmentioning

confidence: 99%

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

Ding

Zhuang

Liu

et al. 2020

Sensors

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Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%.

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“…Such instruments should operate in harsh environments with due precision, reliability and accuracy. Relatively fast measurement time, lightweight and ergonomic design, intuitive user interface and absence of moving parts makes such equipment an interesting alternative for in-field and on-line measurements [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Development of Low-Cost Portable Spectrometers for Detection of Wood Defects

Sandak

Zitek³

et al. 2020

Sensors

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Portable spectroscopic instruments are an interesting alternative for in-field and on-line measurements. However, the practical implementation of visible-near infrared (VIS-NIR) portable sensors in the forest sector is challenging due to operation in harsh environmental conditions and natural variability of wood itself. The objective of this work was to use spectroscopic methods as an alternative to visual grading of wood quality. Three portable spectrometers covering visible and near infrared range were used for the detection of selected naturally occurring wood defects, such as knots, decay, resin pockets and reaction wood. Measurements were performed on wooden discs collected during the harvesting process, without any conditioning or sample preparation. Two prototype instruments were developed by integrating commercially available micro-electro-mechanical systems with for-purpose selected lenses and light source. The prototype modules of spectrometers were driven by an Arduino controller. Data were transferred to the PC by USB serial port. Performance of all tested instruments was confronted by two discriminant methods. The best performing was the microNIR instrument, even though the performance of custom prototypes was also satisfactory. This work was an essential part of practical implementation of VIS-NIR spectroscopy for automatic grading of logs directly in the forest. Prototype low-cost spectrometers described here formed the basis for development of a prototype hyperspectral imaging solution tested during harvesting of trees within the frame of a practical demonstration in mountain forests.

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Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards

Cited by 8 publications

References 14 publications

A review of the automated timber defect identification approach

A review of the automated timber defect identification approach

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

Development of Low-Cost Portable Spectrometers for Detection of Wood Defects

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