Summary of Udeep: Edge-based Computer Vision For In-situ Underwater Crayfish and Plastic Detection, by Dennis Monari et al.
UDEEP: Edge-based Computer Vision for In-Situ Underwater Crayfish and Plastic Detection
by Dennis Monari, Jack Larkin, Pedro Machado, Jordan J. Bird, Isibor Kennedy Ihianle, Salisu Wada Yahaya, Farhad Fassihi Tash, Md Mahmudul Hasan, Ahmad Lotfi
First submitted to arxiv on: 21 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper explores the impact of invasive signal crayfish on ecosystems. The study highlights the devastating effects of these crustaceans on native white clawed crayfish populations, which are already declining at alarming rates due to habitat destruction, pollution, and competition for resources. The paper emphasizes the importance of addressing the challenges posed by invasive species and plastic debris in UK river ecosystems. To achieve this, the UDEEP platform is proposed as a solution, leveraging AI, IoT devices, and edge computing capabilities to monitor and classify signal crayfish and plastic debris in real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Invasive signal crayfish are causing big problems for ecosystems. They spread diseases that can kill native white clawed crayfish, which are already struggling due to habitat destruction, pollution, and competition for food. This is making it hard for the native species to survive. The researchers suggest using a special platform called UDEEP to help monitor and stop the spread of these invasive crayfish. It uses AI and other technologies to quickly identify and track the crayfish and plastic debris. |
