Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

Abdulla, Beshaier A.; Ali, Yossra H.; Ibrahim, Nuha Jameel

doi:10.30684/etj.v38i5a.519

Cited by 1 publication

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“…The accuracy of this method to identify wood knots reaches 92.80%. In 2020, Beshaier and Yossra et al conducted a comparative study on two different image retrieval methods of gray level co-occurrence matrix (GLCM) and Hu invariant moments [19]. The results showed that the average accuracy of GLCM and Hu invariant moments is 92.8% and 84.4%, respectively.…”

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

View full text Add to dashboard Cite

show abstract

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

Cited by 1 publication

References 10 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

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