TEAMER: Support for environmental monitoring and data analysis around a field-deployed tidal energy converter in Sequim Bay, WA

This repository includes data pertaining to the Turbine Lander and Lander Adaptable Monitoring Package (LAMP) deployments in Sequim Bay, WA (2023-2024), which was supported by a TEAMER project.

- Contents -

1. Optical and sonar images and videos pertaining all identified events of interest have been uploaded to the following repository: https://datadryad.org/dataset/doi:10.5061/dryad.d51c5b0dq#readme

Post-processed ADCP data have been uploaded to the same repository in file "Generator_and_Velocity.mat".

2. Object detection models developed during the deployment and processing scripts and test data necessary to evaluate their accuracy and reproduce the performance metrics presented in the TEAMER report.

The test data consist of images and labels. Images are monochrome 8-bit images of objects from the cameras and imaging sonars. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format, described in detail here: https://docs.ultralytics.com/datasets/detect/. Each image has an associated label with 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.

Note: the "train" and "val" directories are also present as the presence of these directories is necessary for the included script to run successfully, though only the "val" data are included.

The model weights files for all models are included in the models directory, e.g., /models/.

Finally, the python script "test_models.py" is included, which loads each model and tests it against the associated test dataset. The resulting accuracy metrics are saved to figures in a ./runs/detect/ directory, which will be created when the script is run on the user's machine. Instructions for installing and using the python script are included in the README.

- Requirements -
All instructions assume the user is using a computer using Ubuntu Linux 20.04+ with Python3.8+. Operation on other operating systems may require some modification to these instructions. Models will run on your computer's CUDA-capable NVIDIA GPU if one is available.
The README.md provides instruction for installing the requirements from the requirements.py file.