Research on image recognition of five bark texture images based on deep learning

Liu Jiazheng; Wang Xuefeng; Wang Tian

doi:10.13332/j.1000-1522.20180242

Journal of Beijing Forestry University > 2019 > 41(4): 146-154. > DOI: 10.13332/j.1000-1522.20180242

Liu Jiazheng, Wang Xuefeng, Wang Tian. Research on image recognition of five bark texture images based on deep learning[J]. Journal of Beijing Forestry University, 2019, 41(4): 146-154. DOI: 10.13332/j.1000-1522.20180242

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Research on image recognition of five bark texture images based on deep learning

Research Institute of Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

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Received Date: July 24, 2018
Revised Date: March 05, 2019
Available Online: April 01, 2019
Published Date: March 31, 2019

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Abstract

Abstract

ObjectiveAiming at the problem that the existing algorithm and recognition process are too complicated in bark image recognition, a method based on deep learning was proposed to identify the bark images of different tree species.
MethodTaking the bark texture image of five common tree species as an example, the deep learning method based on convolutional neural network was used to input the original image directly as input, and the low-level and high-level features of the image were performed by convolution and pooling layer. Automatic extraction solved the difficulty and problem of manually extracting texture features. On this basis, the CNN model structure was improved, and the ELU excitation function with Maxout was used instead of the ReLU function to solve the model offset and zero gradient problem. The loss function was improved, the specification parameters were added to optimize the structural parameters, and the learning rate was dynamically adjusted using the piecewise constant attenuation method. Finally, the softmax classifier was used to output the image categories.
ResultA total of 10 000 images of 5 types of bark texture images were tested, and 200 images of each type were selected as test sets. The final training accuracy rate reached 93.80%, and the test set recognition accuracy rate was 97.70%. In addition, in order to verify the feasibility of the proposed method, and the traditional artificial feature extraction method, the HOG feature, the Gabor feature and the gray level co-occurrence matrix statistical method were extracted, and the SVM classifier was trained. Through experimental comparison, the method identification accuracy was the highest.
ConclusionThe tree-skin image recognition method based on deep learning proposed in this paper is feasible, which improves the recognition efficiency and precision, and provides a new reference for the intelligent identification of tree species.
- deep learning,
- bark image,
- convolutional neural network,
- tree species intelligent recognition

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References (22)

References

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Research on image recognition of five bark texture images based on deep learning