A Fully Convolutional Neural Network for Wood Defect Location and Identification

He, Ting; Liu, Ying; Xu, Chuan; Zhou, Xiaolin; Hu, Zhongkang; Fan, Jiaqi

doi:10.1109/access.2019.2937461

Cited by 75 publications

(44 citation statements)

References 32 publications

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“…Therefore, a convolution neural network that can learn wood knot defect features automatically, instead of complex artificial extraction for defect detection, is needed. A fully convolutional neural network (Mix-FCN) was proposed by He et al in 2019 to identify and locate wood defects [21], but the depth of the network is too deep, resulting in too much computation. In 2020, an improved SSD algorithm was proposed by Ding et al [22].…”

Section: Introductionmentioning

confidence: 99%

A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

Gao

et al. 2021

Forests

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In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.

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Section: Introductionmentioning

confidence: 99%

A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

Gao

et al. 2021

Forests

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“…Performing experiments in such conditions usually entails the disadvantage of a limited number of available products. In most of the studies, 2,6,10,11 researchers compensate for the lack of real products by using data augmentation techniques, which can expand the dataset up to 10 times its original size. From one point of view, data augmentation is considered to be an excellent tool to generalize the classification model and therefore prevent overfitting.…”

Section: Introductionmentioning

confidence: 99%

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

2021

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The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.

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“…Comparing the performance of the three CNN models, the deep CNN achieved a high classification accuracy of 99.8% for defect detection, but its running speed was slow because the network went deeper. He et al [19] used a mixed fully convolutional neural network (Mix-FCN) to locate and classify wood defects with the wood surface images automatically. However, the Mix-FCN used all of the computing resources for all of the wood regardless of the majority of wood images being free of defects, which was inefficient time-wise.…”

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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A Fully Convolutional Neural Network for Wood Defect Location and Identification

Cited by 75 publications

References 32 publications

A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

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

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