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    • Fish Detection AI, Optic and Sonar-trained Object Detection Models

    Metadata Updated: May 22, 2025

    The Fish Detection AI project aims to improve the efficiency of fish monitoring around marine energy facilities to comply with regulatory requirements. Despite advancements in computer vision, there is limited focus on sonar images, identifying small fish with unlabeled data, and methods for underwater fish monitoring for marine energy.

    A YOLO (You Only Look Once) computer vision model was developed using the Eyesea dataset (optical) and sonar images from Alaska Fish and Games to identify fish in underwater environments. Supervised methods were used within YOLO to detect fish based on training using labeled data of fish. These trained models were then applied to different unseen datasets, aiming to reduce the need for labeling datasets and training new models for various locations. Additionally, hyper-image analysis and various image preprocessing methods were explored to enhance fish detection.

    In this research we achieved: 1. Enhanced YOLO Performance, as compared to a published article (Xu, Matzner 2018) using earlier yolo versions for fish object identification. Specifically, we achieved a best mean Average Precision (mAP) of 0.68 on the Eyesea optical dataset using YOLO v8 (medium-sized model), surpassing previous YOLO v3 benchmarks from that previous article publication. We further demonstrated up to 0.65 mAP on unseen sonar domains by leveraging a hyper-image approach (stacking consecutive frames), showing promising cross-domain adaptability.

    This submission of data includes: - The actual best-performing trained YOLO model neural network weights, which can be applied to do object detection (PyTorch files, .pt). These are found in the Yolo_models_downloaded zip file - Documentation file to explain the upload and the goals of each of the experiments 1-5, as detailed in the word document (named "Yolo_Object_Detection_How_To_Document.docx") - Coding files, namely 5 sub-folders of python, shell, and yaml files that were used to run the experiments 1-5, as well as a separate folder for yolo models. Each of these is found in their own zip file, named after each experiment - Sample data structures (sample1 and sample2, each with their own zip file) to show how the raw data should be structured after running our provided code on the raw downloaded data - link to the article that we were replicating (Xu, Matzner 2018) - link to the Yolo documentation site from the original creators of that model (ultralytics) - link to the downloadable EyeSea data set from PNNL (instructions on how to download and format the data in the right way to be able to replicate these experiments is found in the How To word document)

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons Attribution

    Downloads & Resources

    Dates

    Metadata Created Date May 22, 2025
    Metadata Updated Date May 22, 2025

    Metadata Source

    Harvested from OpenEI data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date May 22, 2025
    Metadata Updated Date May 22, 2025
    Publisher Water Power Technology Office
    Maintainer
    Identifier https://data.openei.org/submissions/8419
    Data First Published 2014-06-25T06:00:00Z
    Data Last Modified 2025-05-21T15:42:18Z
    Public Access Level public
    Bureau Code 019:20
    Metadata Context https://openei.org/data.json
    Metadata Catalog ID https://openei.org/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Data Quality True
    Harvest Object Id c2b17ff3-8b1c-4399-ab21-425231a1af81
    Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
    Harvest Source Title OpenEI data.json
    Homepage URL https://mhkdr.openei.org/submissions/600
    License https://creativecommons.org/licenses/by/4.0/
    Old Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}
    Program Code 019:009
    Projectlead Samantha Eaves
    Projectnumber 32326
    Projecttitle Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (WPTO)
    Source Datajson Identifier True
    Source Hash e5e85510dcbdc9a20b7bd480cac8c921886f18084e56f7699befdfb40221b6eb
    Source Schema Version 1.1
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