RETRACTED: Artificial neural network for defect detection in CT images of wood

Pan, Ligong; Rogulin, Rodion; Kondrashev, Sergey

doi:10.1016/j.compag.2021.106312

Cited by 21 publications

(15 citation statements)

References 29 publications

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“…Storage limits and inventory management [7], [16], [95], [110], [113], [114] [9], [16], [17], [19], [28], [34], [85], [87], [115], [116], [107], [108], [112], [114] [5], [8], [15], [28], [31], [34], [85], [117] Order lead time [5], [14], [41] Log / lumber price [86], [123] Scanning technology limits no longer valid once any part of the log deviates from the cylindrical definition [95]. Hence, this sort of assumptions is inaccurate and can waste a large amount of the potential yield as the sawing process creates low-price by-products or sawdust.…”

Section: Materials Variabilitymentioning

confidence: 99%

Wood Products Manufacturing Optimization: A Survey

Hosseini

Peer

2022

IEEE Access

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The wood industry is a basic industry supplying primary materials to produce a wide range of products. The wood processing in sawmills bridges the transformation flow of raw materials to final products using machinery. Over the last decade, the global market for wood products has become highly competitive. As a result, sawmills are continually striving to improve their efficiency throughout their production process. Recent advances in automation systems and manufacturing technologies provide traditional sawmills with an unprecedented opportunity to be transformed into automated manufacturing plants to increase their efficiency. This opportunity motivates sawmill planners to adopt decision optimization technologies for enhancing their production yields. This transformation will support sawmills to remain resilient in current and future competitive markets. Although there is a vast amount of literature on decision-making in wood manufacturing in sawmills, a comprehensive overview of related approaches and findings with a special focus on optimization of decision processes is still lacking. The review firstly focuses on exploring opportunities provided by optimizing decision-making processes in the wood manufacturing along with associated roadblocks and challenges. Then, we explore methodologies adopted in the literature when aiming for the design and implementation of optimal decision-making systems. Finally, we provide references and guidelines for researchers and manufacturers interested in the topic, as well as formulate research gaps and recommendations for future research directions.INDEX TERMS autonomous production machines, automated wood processing, decision-making, decision support systems, optimization

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Section: Materials Variabilitymentioning

confidence: 99%

Wood Products Manufacturing Optimization: A Survey

Hosseini

Peer

2022

IEEE Access

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“…There are also studies that detect defects on different materials using CNN. Defect-detection studies are on glass panels [24], wood [25], and fabric [26][27][28][29][30].…”

Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the nonuniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem have been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models.

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“…Detecting wood defects in the shortest possible time leads to finding efficient log processing solutions. An efficient method of automatic detection and recognition of wood defects, using a neural network, was discovered by Pan et al in 2021(Pan, 2021. The network is trained with CT images, has several hidden layers and uses a reinforcement learning strategy.…”

Section: Image Processing With Ann In Civil Engineeringmentioning

confidence: 99%

A Study on Image Processing Using Artificial Neural Networks in Civil Engineering

Andon

Covatariu

2022

Bulletin of the Polytechnic Institute of Iași. Construction. Architecture Section

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For the last five years, image processing using artificial neural networks (ANN) got several researchers interested in the field of Civil Engineering. As the artificial neural network, which consists of several neurons may not be able to extract features from the image due to the required computing power, the Convolutional Neural Network (CNN) was created. CNN is a machine learning algorithm that uses the image as input, attributing the importance of different aspects (objects in the image) to allow their differentiation. The results recorded in Civil Engineering domain show a real success.

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RETRACTED: Artificial neural network for defect detection in CT images of wood

Cited by 21 publications

References 29 publications

Wood Products Manufacturing Optimization: A Survey

Wood Products Manufacturing Optimization: A Survey

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

A Study on Image Processing Using Artificial Neural Networks in Civil Engineering

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