Underwater Target Detection Software Demonstration on the RivGen Turbine
This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95).
- Contents -
Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK.
Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine.
Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format.
- Requirements -
Python3.8+ is required to install and run the train and validation script.
The README.md provides instruction for installing the requirements from the requirements.py file.
- Instructions -
The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.
Citation Formats
TY - DATA
AB - This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95).
- Contents -
Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK.
Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine.
Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format.
- Requirements -
Python3.8+ is required to install and run the train and validation script.
The README.md provides instruction for installing the requirements from the requirements.py file.
- Instructions -
The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.
AU - Joslin, James
A2 - Murphy, Paul
A3 - Runyan, Alexa
A4 - Scott, Mitchell
DB - Marine and Hydrokinetic Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15473/2488381
KW - MHK
KW - Marine
KW - Hydrokinetic
KW - ML
KW - Machine Learning
KW - Igiugig
KW - Alaska
KW - Detection
KW - Cross-flow
KW - Tracking
KW - Smolt
KW - Salmon
KW - data
KW - processing scripts
KW - Python
KW - perfomance metrics
KW - TEAMER
KW - underwater target detection
KW - images
LA - English
DA - 2024/12/17
PY - 2024
PB - MarineSitu
T1 - Underwater Target Detection Software Demonstration on the RivGen Turbine
UR - https://doi.org/10.15473/2488381
ER -
Joslin, James, et al. Underwater Target Detection Software Demonstration on the RivGen Turbine. MarineSitu, 17 December, 2024, Marine and Hydrokinetic Data Repository. https://doi.org/10.15473/2488381.
Joslin, J., Murphy, P., Runyan, A., & Scott, M. (2024). Underwater Target Detection Software Demonstration on the RivGen Turbine. [Data set]. Marine and Hydrokinetic Data Repository. MarineSitu. https://doi.org/10.15473/2488381
Joslin, James, Paul Murphy, Alexa Runyan, and Mitchell Scott. Underwater Target Detection Software Demonstration on the RivGen Turbine. MarineSitu, December, 17, 2024. Distributed by Marine and Hydrokinetic Data Repository. https://doi.org/10.15473/2488381
@misc{MHKDR_Dataset_588,
title = {Underwater Target Detection Software Demonstration on the RivGen Turbine},
author = {Joslin, James and Murphy, Paul and Runyan, Alexa and Scott, Mitchell},
abstractNote = {This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95).
- Contents -
Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK.
Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine.
Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format.
- Requirements -
Python3.8+ is required to install and run the train and validation script.
The README.md provides instruction for installing the requirements from the requirements.py file.
- Instructions -
The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.
},
url = {https://mhkdr.openei.org/submissions/588},
year = {2024},
howpublished = {Marine and Hydrokinetic Data Repository, MarineSitu, https://doi.org/10.15473/2488381},
note = {Accessed: 2025-04-26},
doi = {10.15473/2488381}
}
https://dx.doi.org/10.15473/2488381
Details
Data from Dec 17, 2024
Last updated Jan 3, 2025
Submitted Dec 17, 2024
Organization
MarineSitu
Contact
James Josline
360.477.2901
Authors
Keywords
MHK, Marine, Hydrokinetic, ML, Machine Learning, Igiugig, Alaska, Detection, Cross-flow, Tracking, Smolt, Salmon, data, processing scripts, Python, perfomance metrics, TEAMER, underwater target detection, imagesDOE Project Details
Project Name Testing Expertise and Access for Marine Energy Research
Project Lead Lauren Ruedy
Project Number EE0008895
