Wood surface defect detection based on image fusion

Man-li, 李漫丽 Li; Peng, 赵鹏 Zhao

doi:10.3788/yjyxs20163109.0882

Cited by 1 publication

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“…The aforementioned methods need to extract multiple features, leading some researchers to focus on the research feature fusion of wood defect. Li et al studied the application of the weighted average scheme, PCA scheme, wavelet transform scheme, and Laplacian pyramid scheme to image feature fusion [7]. Wu and Ye applied affinity propagation clustering to detect wood defect [8].…”

Section: Introductionmentioning

confidence: 99%

A Fully Convolutional Neural Network for Wood Defect Location and Identification

Liu

et al. 2019

IEEE Access

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Defect detection on solid wood surface has two main problems: (1) the real-time performance of the available methods are poor despite good detection accuracy, and (2) the defect extraction process is complicated. Here, we propose a mixed, fully convolutional neural network (Mix-FCN) to detect the location of wood defects and classify the types of defects from the wood surface images automatically. The images were collected first by a data acquisition device developed in our laboratory. We then employed TensorFlow and Python language to construct a VGG16 model. We used two kinds of datasets (dataset1 and dataset2) to maximize the limited, collected data and enable the Mix-FCN to converge rapidly during training. The weights of the filters in front of the Mix-FCN during training were initialized from the trained VGG16 model. The weights of the VGG16 net were learned by dataset1. Our model was trained, validated, and tested by dataset 2. Overall classification accuracy (OCA), pixel accuracy (PA), mean intersection over union, detection rate, missing alarm, false alarm rate, and precision were used to evaluate the network, and the performance was good based on the seven evaluation indicators. We achieved 99.14% OCA and 91.31% PA, and a batch of 50 images required only 0.368 s of detection time. Our proposed method has better accuracy and less detection time compared to the previous methods of wood detection. INDEX TERMS Deep learning, full convolutional neural network, transfer learning, wood defects detection.

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Section: Introductionmentioning

confidence: 99%

A Fully Convolutional Neural Network for Wood Defect Location and Identification

Liu

et al. 2019

IEEE Access

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Wood surface defect detection based on image fusion

Cited by 1 publication

References 0 publications

A Fully Convolutional Neural Network for Wood Defect Location and Identification

A Fully Convolutional Neural Network for Wood Defect Location and Identification

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