수피 특징 추출을 위한 상용 DCNN 모델의 비교와 다층 퍼셉트론을 이용한 수종 인식Comparison of Off-the-Shelf DCNN Models for Extracting Bark Feature and Tree Species Recognition Using Multi-layer Perceptron
- Other Titles
- Comparison of Off-the-Shelf DCNN Models for Extracting Bark Feature and Tree Species Recognition Using Multi-layer Perceptron
- Issue Date
- Tree Species Identification; ResNet50; DCNN; MLP
- 멀티미디어학회논문지, v.23, no.9, pp.1155 - 1163
- Journal Title
- Start Page
- End Page
- 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 respectivel
- Files in This Item
- There are no files associated with this item.
- Appears in
- ETC > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.