Summary of Lightweight Fish Classification Model For Sustainable Marine Management: Indonesian Case, by Febrian Kurniawan et al.
Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case
by Febrian Kurniawan, Gandeva Bayu Satrya, Firuz Kamalov
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers propose a machine learning-based approach to classify fish species and determine their consumability, aiming to support sustainable fishing practices and protect marine resources. The team designs a lightweight classifier called M-MobileNet, modified from the MobileNet model, which can run on limited hardware. A labeled dataset of 37,462 images of fish from the Indonesian archipelago is compiled for training. The proposed model achieves up to 97% accuracy in fish classification and consumability determination, making it a practical solution for on-site fish classification. Additionally, the study demonstrates the potential for synchronized implementation across multiple vessels to provide valuable insights into fish movement and location. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to identify different types of fish and decide if they are safe to eat. The scientists use special computer algorithms called machine learning models to create a “fish classifier” that can work even on small computers, like those found on fishing boats. They made a big dataset of pictures of different fish species from the waters around Indonesia and used it to train their model. The new system is very good at identifying what kind of fish it is and whether or not you should eat it. This could help people make better choices about what fish they catch, which would be good for both humans and the environment. |
Keywords
* Artificial intelligence * Classification * Machine learning
