Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme

Zhao, Peng; Li, Yue; Xiao, Ning Di

doi:10.1155/2016/7049523

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…To enhance the application of wood surface laser scanning technology in practical wood processing production, Wang et al (2016) designed a device that could achieve laser scanning of wood flow lines. Peng et al (2016) used a portable handheld laser scanner to scan the wood surface and achieved the first simultaneous identification of wood surface defects and wood species, and the accuracy rate could reach around 95% (Peng et al 2016).…”

Section: Fig 2 Different Amounts Of Data Between Deep Learning and Or...mentioning

confidence: 99%

Review of the current state of application of wood defect recognition technology

Chen

Sun

Ren

et al. 2022

BioRes

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Wood utilisation is an important factor affecting production costs, but the combined utilisation rate of wood is generally only 50 to 70%. During the production process, the rejection scheme of wood defects is one of the most important factors affecting the wood yield. This paper provides an overview of the main wood defects affecting wood quality, introduces techniques for detecting and identifying wood defects using different technologies, highlights the more widely used image recognition-based wood surface defect identification methods, and presents three advanced wood defect detection and identification equipment. In view of the relatively fixed wood defect recognition requirements in wood processing production, it is proposed that wood defect recognition technology should be further developed toward deep learning to improve the accuracy and efficiency of wood defect recognition.

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Section: Fig 2 Different Amounts Of Data Between Deep Learning and Or...mentioning

confidence: 99%

Review of the current state of application of wood defect recognition technology

Chen

Sun

Ren

et al. 2022

BioRes

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“…Many methods can be used for detecting and classifying wood surface colors using machine vision [ 15 ]. However, only few color classifications for the entire board are currently available.…”

Section: Introductionmentioning

confidence: 99%

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

Zhuang

Liu

Ding

et al. 2021

Sensors

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Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.

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“…A fast wood defect detection method is necessary for modern wood veneer processing industries to improve their wood use rate and increase their revenue. Currently, many kinds of technologies have been used to detect defects on wood veneers, including air-coupled ultrasonic technology [3], stress wave technology [4], 3D laser technology [5], computed tomography [6], and computer vision technology [7]. Air-coupled ultrasonic technology is a contactless ultrasonic measurement method that detects defects inside the wood veneer based on defects' densities variations inside the wood.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

Shi

Zhu

et al. 2020

Sensors

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Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

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Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme

Cited by 13 publications

References 14 publications

Review of the current state of application of wood defect recognition technology

Review of the current state of application of wood defect recognition technology

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

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