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http://dx.doi.org/10.9717/kmms.2020.23.9.1155

Comparison of Off-the-Shelf DCNN Models for Extracting Bark Feature and Tree Species Recognition Using Multi-layer Perceptron  

Kim, Min-Ki (Dept. of Computer Engineering, Gyeongsang National University Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.23, no.9, 2020 , pp. 1155-1163 More about this Journal
Abstract
Deep learning approach is emerging as a new way to improve the accuracy of tree species identification using bark image. However, the approach has not been studied enough because it is confronted with the problem of acquiring a large volume of bark image dataset. This study solved this problem by utilizing a pretrained off-the-shelf DCNN model. It compares the discrimination power of bark features extracted by each DCNN model. Then it extracts the features by using a selected DCNN model and feeds them to a multi-layer perceptron (MLP). We found out that the ResNet50 model is effective in extracting bark features and the MLP could be trained well with the features reduced by the principal component analysis. The proposed approach gives accuracy of 99.1% and 98.4% for BarkTex and Trunk12 datasets respectively.
Keywords
Tree Species Identification; ResNet50; DCNN; MLP;
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Times Cited By KSCI : 4  (Citation Analysis)
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