On-Deck Fish Detection Object Detection Dataset and Pre-Trained Model by E4E

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Explore Dataset

Here are a few use cases for this project:

Fisheries Management: The model could be used in the fishing industry to automatically identify and count fish during haul processing. This could help in collecting fisheries data, managing fish populations, and implementing sustainable fishing practices.
Aquaculture Operations: The model can be integrated into aquaculture management systems to identify and track individuals within fish stocks. This could provide valuable information for feeding, health assessment, and growth tracking of fish.
Marine Biology Research: Researchers could use this tool to automate fish identification and quantification in field studies, thus improving the efficiency and accuracy of data collection in marine ecology and biology projects.
Commercial Fishing: The model can aid in automated sorting of catch onboard commercial fishing vessels. By identifying fish from head to tail, it allows for accurate catch classification, enabling fishermen to easily sort their catch and comply with fishing regulations.
Underwater Monitoring Systems: The model could be integrated into underwater surveillance systems at protected marine sites to quickly identify any fish species present, thus helping to detect any unwanted or invasive species immediately.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            on-deck-fish-detection_dataset,
                            title = { On-Deck Fish Detection Dataset },
                            type = { Open Source Dataset },
                            author = { E4E },
                            howpublished = { \url{ https://universe.roboflow.com/e4e/on-deck-fish-detection } },
                            url = { https://universe.roboflow.com/e4e/on-deck-fish-detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { aug },
                            note = { visited on 2024-06-01 },
                            }