碩士 / 國立臺灣大學 / 生物產業機電工程學研究所 / 105 / In recent years, international organizations have regulated fishery in public seas to conserve marine ecosystems. Automatically identifying the types of fish catch is one of the most impartial approaches for monitoring fishing practices. Hence, this study proposes the use of deep learning algorithms to automatically identify major fish catch of Taiwan. Through the use of transfer learning, two convolutional neural network classifiers were developed to differentiate fish into four classes: tuna, billfish, shark, and others. During this process, two pre-trained models were employed as the base network and then fine-tuned to improve the identification ability when facing fine-grained-image classification problems. The experimental results show that these four classes of fish can be identified with a relatively high degree of accuracy (97.5% for the VGG16-based classifier and 96.75% for the Inception-V3-based classifier).
| Identifer | oai:union.ndltd.org:TW/105NTU05415024 |
| Date | January 2017 |
| Creators | Chen Tung, 董真 |
| Contributors | 郭彥甫 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 25 |
Page generated in 0.0076 seconds