Wood species recognition from wood images with an improved CNN1

Miao, Yongwei; Zhu, Shiping; Hua, Huang; Li, Junxian; Xiao, Wei; Ma, Lingkai; Pu, Jing

doi:10.3233/jifs-211097

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…However, it is crucial to identify and classify wood accurately and efficiently to prevent the prevalence of counterfeit and substandard wood, thereby safeguarding consumer rights and interests [1]. Manual inspection methods, which rely on subjective judgment, are inefficient and contradict the objective of wood classification.…”

Section: Introductionmentioning

confidence: 99%

A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images

Zheng,

Ge,

Yang

et al. 2024

Preprint

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To address the issue of limited expressive ability and performance degradation of the model caused by the limited depth and width of the CNN because of low computational overhead of lightweight convolutional neural networks, this paper introduces a classification method called A-RepVGG for wood CT images. A-RepVGG aims to enhance the model's classification accuracy in terms of wood microstructure by increasing the model complexity without increasing the depth and width of the network. The method utilizes adaptive convolution to dynamically aggregate convolution kernels based on the input image. This allows the convolution kernels to have optimal receptive fields on different layers of features, enabling a more comprehensive extraction of information, such as wood texture, tubular pore distribution, and cell arrangement. Additionally, a multi-scale null attention mechanism is incorporated into the network, multi-layer convolution is employed to extract feature maps of different scales and weighted fusion is adopted to emphasize important feature regions. This effectively captures both local and global information of the image. Finally, the ELU activation function is introduced to ensure that the feature information can be properly output in the negative half-axis, thereby facilitating a more thorough extraction of wood feature information and improving the model's classification accuracy. The study aimed to classify 20 species of wood cross-section microscopic images. The findings revealed that the A-RepVGG model outperformed other existing wood image classification models, such as ResNet, ResNeSt, and ViT. The A-RepVGG model achieved an impressive accuracy of 99.50% on the test set and 99.20% on the validation set. This model incorporates adaptive convolution and attention mechanisms, which effectively enhance the classification accuracy of wood microscopic images. These results highlight the potential of deep learning in automatic classification of wood species and provide valuable insights for micro-level wood classification.

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Section: Introductionmentioning

confidence: 99%

A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images

Zheng,

Ge,

Yang

et al. 2024

Preprint

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An accurate object detection of wood defects using an improved Faster R-CNN model

Zou,

Wu,

Liu

et al. 2024

Wood Material Science & Engineering

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Wood identification based on macroscopic images using deep and transfer learning approaches

Ergun

2024

PeerJ

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Identifying forest types is vital for evaluating the ecological, economic, and social benefits provided by forests, and for protecting, managing, and sustaining them. Although traditionally based on expert observation, recent developments have increased the use of technologies such as artificial intelligence (AI). The use of advanced methods such as deep learning will make forest species recognition faster and easier. In this study, the deep network models RestNet18, GoogLeNet, VGG19, Inceptionv3, MobileNetv2, DenseNet201, InceptionResNetv2, EfficientNet and ShuffleNet, which were pre-trained with ImageNet dataset, were adapted to a new dataset. In this adaptation, transfer learning method is used. These models have different architectures that allow a wide range of performance evaluation. The performance of the model was evaluated by accuracy, recall, precision, F1-score, specificity and Matthews correlation coefficient. ShuffleNet was proposed as a lightweight network model that achieves high performance with low computational power and resource requirements. This model was an efficient model with an accuracy close to other models with customisation. This study reveals that deep network models are an effective tool in the field of forest species recognition. This study makes an important contribution to the conservation and management of forests.

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Wood species recognition from wood images with an improved CNN1

Cited by 3 publications

References 26 publications

A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images

A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images

An accurate object detection of wood defects using an improved Faster R-CNN model

Wood identification based on macroscopic images using deep and transfer learning approaches

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