Fish classification and deep learning
Nowadays, more and more aquariums prefer to use scientific and technological methods to perform fish classification rather than identification relying on manual. The biggest problem is how to get a good dataset for training and to find a reliable method to identify fish categories with high acc...
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| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/168085 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Nowadays, more and more aquariums prefer to use scientific and technological
methods to perform fish classification rather than identification relying on manual. The
biggest problem is how to get a good dataset for training and to find a reliable method
to identify fish categories with high accuracy. Most of the traditional methods focus
on the pretreatment of dataset and physical condition of hardware. While this essay
aims to perform fish classification by a unified meta framework for fine-grained
recognition. In data collection part, we include 806 images from aquarium dataset and
1608 images from public dataset. After pretreatment of dataset and cropping operation
based on YOLOv7, high classification accuracies for tank A, B, C, and D with 90.14%,
81.74%, 92.81%, and 80.35% are achieved for this meta framework. |
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