Automatic Classification of Wood Defects Using Support Vector Machines

Gu, Irene Yu‐Hua; Andersson, Henrik; Vicen, Raul

doi:10.1007/978-3-642-02345-3_35

“…Firstly, they can only detect surface defects but not inner defects. Secondly, timber panels are unique even if they originate from the same tree, which 2 of 11 requires the application of advanced pattern recognition methods for defect detection [8]. Some techniques, such as those using sonic stress waves, electrical resistivity, or ultrasound, need direct contact or a coupling agent between the transducers and the sample, which makes the inspection process cumbersome and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques

Jiang,

Wang,

Zhang

et al. 2024

Applied Sciences

2

0

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This paper reports on investigations of the air-coupled ultrasonic (ACU) method to detect common defects in solid timber panels made of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). The ACU technology is a non-contact method for nondestructive timber testing with quicker scanning rates compared to contact methods. A testbed was set up consisting of commercially available piezo-ceramic ACU transducers and in-house manufactured signal processing circuits. To demonstrate the suitability of the ACU technique, through-transmission measurement results are presented for samples with defects such as knots, wormholes, and cracks. Pulse compression methods (Barker-coded method) were used to improve the power of received signals based on cross-correction algorithms. Results showed defects of timber panels made of Chinese fir can be detected with a thickness of less than 40 mm. Defects larger than 3 mm in diameter could be detected with high precision. Applying the pulse compression method showed better results than using common sine signals as excitation signals since it increased the signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like timber materials. The measurement results on reference samples demonstrated that ACU technology is a promising method for timber defect detection, especially for the quality assessment of engineered wood products.

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“…Even though the automation in this industrial sector is growing, many market leader companies still utilize trained domain experts to detect undesirable features and to perform quality grading. 5 Besides the fact that the manual examination is tedious and biased, it was found that domain experts are not able to check large production volumes. Moreover, the study conducted by Urbonas et al .…”

Section: Introductionmentioning

confidence: 99%

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

Kodytek

¹

,

Bodzas

²

,

Bilík

³

2022

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The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.

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“…3,4 Even though the automation in this industrial sector is growing, many market leader companies still utilize trained domain experts to detect undesirable features and to perform quality grading. 5 Besides the fact that the manual examination is tedious and biased, it was found that domain experts are not able to check large production volumes. Moreover, the study conducted by Urbonas et al 6 stated that due to factors such as eye fatigue or distraction, manual inspection rarely achieves 70 % reliability.…”

Section: Introductionmentioning

confidence: 99%

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

Kodytek

¹

,

Bodzas

²

,

Bilík

³

2021

View full text Add to dashboard Cite

The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.

show abstract

Automatic Classification of Wood Defects Using Support Vector Machines

Cited by 16 publications

References 4 publications

Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques

Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

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