Classification of Knot Defect Types Using Wavelets and KNN

Çetiner, İbrahim; Var, Ahmet; ÇETİNER, Halit

doi:10.5755/j01.eie.22.6.17227

Cited by 23 publications

(16 citation statements)

References 13 publications

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“…Finally, defect images were classified based on a KNN algorithm with an average accuracy rate of 96%. Cetiner et al [158] proposed a method of feature extraction based on the wavelet moment and defect image classification based on KNN, which can be used in automatic defect classification systems in the forest industry. Das and Jena [159] presented a method combining image texture feature extraction techniques.…”

Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

284

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

284

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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

114

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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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“…Schmoldt et al (1997) investigated using a multi-layer perceptron neural network to identify and locate internal log defects. In addition, techniques such as Gabor or wavelet can also be used to scan products and report defects (Lampinen et al 1998;Cetiner et al 2016). However, expensive equipment and several steps are necessary for defect classification and detection.…”

Section: Introductionmentioning

confidence: 99%

A fast and robust artificial intelligence technique for wood knot detection

Lopes

Bobadilha

Grebner

2020

BioRes

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This study reports the feasibility of using deep convolutional neural networks (CNN), for automatically detecting knots on the surface of wood with high speed and accuracy. A limited dataset of 921 images were photographed in different contexts and divided into 80:20 ratio for training and validation, respectively. The “You only look once” (YoloV3) CNN-based architecture was adopted for training the neural network. The Adam gradient descent optimizer algorithm was used to iteratively minimize the generalized intersection-over-union loss function. Knots on the surface of wood were manually annotated. Images and annotations were analyzed by a stack of convolutional and fully connected layers with skipped connections. After training, model checkpoint was created and inferences on the validation set were made. The quality of results was assessed by several metrics: precision, recall, F1-score, average precision, and precision x recall curve. Results indicated that YoloV3 provided knot detection time of approximately 0.0102 s per knot with a relatively low false positive and false negative ratios. Precision, recall, f1-score metrics reached 0.77, 0.79, and 0.78, respectively. The average precision was 80%. With an adequate number of images, it is possible to improve this tool for use within sawmills in the forms of both workstation and mobile device applications.

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Classification of Knot Defect Types Using Wavelets and KNN

Cited by 23 publications

References 13 publications

State of the Art in Defect Detection Based on Machine Vision

State of the Art in Defect Detection Based on Machine Vision

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

A fast and robust artificial intelligence technique for wood knot detection

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