Literature Review and Comparison of Wood Testing Methods and Research on Application Technology of Computer 3D Scanning Detection

Jiang, Shufeng; Zhou, Ziye; Wang, Keqi

doi:10.1145/3349341.3349443

Cited by 1 publication

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“…However, the acquisition of stress wave signals requires the sensors to be firmly attached to the surface of the wood and the distance between sensors must be fixed, which restricts the efficiency of defect detection and makes this method not flexible enough for industry usage [9]. 3D laser technology can be used to accurately measure the shape of wood surfaces and identify defects with 3D scanner technology in terms of area and volume [10]. However, some defects cannot be detected accurately by the 3D laser scanner when their shapes are very small.…”

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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Section: Introductionmentioning

confidence: 99%