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deepsea-detect (MATE 2022 ML Challenge) Computer Vision Project

UWROV 2022 ML Challenge

Updated 3 years ago

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Object Detection Model
Classes (9)
annelida
arthropoda
cnidaria
echinodermata
fish
mollusca
other-invertebrates
porifera
unidentified-biology

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Description

Here are a few use cases for this project:

  1. Biodiversity Monitoring and Conservation: Researchers and environmental organizations can use this computer vision model to automatically analyze underwater images or videos to identify and monitor the presence and distribution of various marine organism species. This information can support conservation efforts by providing insights into the health of ecosystems and identifying areas in need of protection.

  2. Environmental Impact Assessments: Deepsea-detect can be employed by companies and regulatory agencies to assess the impacts of human activities on marine life. For example, analyses of underwater footage from the vicinity of proposed construction sites, oil drilling operations, or waste disposal areas can help stakeholders make informed decisions about potential effects on marine species.

  3. Aquarium Management and Education: Aquariums can use the model to identify and monitor the health of diverse marine organisms within their exhibits. Automatically recognizing and tracking species in tanks can help ensure proper care and feeding. Additionally, integrating this technology into interactive displays can enhance visitors' educational experiences by providing real-time information about various species.

  4. Underwater Robotics and Exploration: Autonomous underwater vehicles and remotely operated vehicles equipped with deepsea-detect can identify and catalog marine organisms in real-time during deep-sea exploration missions. This capability can support scientific research, natural resource discovery (e.g., hydrothermal vents), or even marine archeology projects.

  5. Marine Biology Research and Education: Deepsea-detect can be employed as a valuable tool for marine biology researchers and students to assist in the identification and classification of marine organisms. By automating the identification process, researchers can quickly analyze large datasets, enabling them to focus on analyzing patterns and trends in marine biodiversity.

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Cite This Project

LICENSE
BY-NC-SA 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            deepsea-detect--mate-2022-ml-challenge_dataset,
                            title = { deepsea-detect (MATE 2022 ML Challenge) Dataset },
                            type = { Open Source Dataset },
                            author = { UWROV 2022 ML Challenge },
                            howpublished = { \url{ https://universe.roboflow.com/uwrov-2022-ml-challenge/deepsea-detect--mate-2022-ml-challenge } },
                            url = { https://universe.roboflow.com/uwrov-2022-ml-challenge/deepsea-detect--mate-2022-ml-challenge },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jun },
                            note = { visited on 2024-12-24 },
                            }

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