Research on recognition of wood texture based on integrated neural network classifier

Wang, Yeqin; Wang, Hui; Mo, Lihong

doi:10.1109/icicip.2010.5564210

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Computer vision systems using high-resolution and high-speed video cameras scan products and report on product quality through various algorithms and techniques. Several methods can be used for surface analysis, such as Gabor [4] or wavelet [5] transforms, and deep learning techniques [6][7][8][9][10], but they are not always sufficiently accurate or fast. For example, the double threshold method is used to check the quality of materials in the textile industry [11] to reduce the amount of data in the images used for the training of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

Urbonas¹,

Raudonis²,

Maskeliūnas

et al. 2019

Applied Sciences

101

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In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

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