Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

Wang, Chengcheng; Liu, Yaqiu; Wang, Pei-Yu

doi:10.3390/a12010006

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Wang et al [61] The authors proposed a method based on the kernel correlation filter (KCF) target tracking algorithm for the detection of surface defects in moving particleboards on the production line.…”

Section: Accuracy Is Not Specifiedmentioning

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

113

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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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Section: Accuracy Is Not Specifiedmentioning

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

113

View full text Add to dashboard Cite

show abstract

“…Shi et al (2020) constructed a convolutional neural network and then used multi-channel Mask R-CNN to classify and locate defects, which can identify dead knots, live knots, and cracks in wood. Wang et al (2018) used the fuzzy pattern recognition method to detect the surface defects of particleboard in motion and calculated the number of defects, defect area, and damage degree. Yang et al (2019) used a 3D laser sensor system to classify and identify the surface defects of wood-based panels and obtained a final classification accuracy of 94.7% after applying SVM.…”

Section: Introductionmentioning

confidence: 99%

Surface defect detection method of wooden spoon based on improved YOLOv5 algorithm

Tian,

Li,

Fang

et al. 2023

BioRes

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The available surface defect detection methods for disposable wooden spoons still involve screening with the naked eye. This detection method is not only inefficient but also accompanied by problems such as false detection and missed detection. Therefore, this paper proposes a detection method based on an improved YOLOv5 network model (YOLOv5-TSPP). This method uses the K-Means ++ algorithm to cluster the target samples in the data set to obtain anchor frames that are more in line with different target scales. The Coordinate Attention module is added to the backbone network of the YOLOv5 network model to improve the feature extraction ability of the model. A new SPP module is added to the backbone network to increase the important features in the receptive field extraction network to improve the detection accuracy of small targets. The experimental results show: The YOLOv5-TSPP algorithm has better detection performance and the mAP of defect detection reaches 80.3%, which is 9.2% higher than that of the YOLOv5 algorithm. Among them, the detection accuracy of black knot defect reached 98.6%, the detection accuracy of back crack defect reached 92.1%, and the detection accuracy of mineral line defect reached 92.3%.

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Defect Detection of Particleboards by Visual Analysis and Machine Learning

Prasitmeeboon

Yau

2019

2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)

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Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

Cited by 5 publications

References 30 publications

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

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

Surface defect detection method of wooden spoon based on improved YOLOv5 algorithm

Defect Detection of Particleboards by Visual Analysis and Machine Learning

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