MIT Fishery Counter

Contributors

• Robert Vincent (Research Advisor)
• Austin Powell (Contributor)
• Lydia Zuehsow (Contributor)

Project Description

Fisheries populations have a large impact on the U.S. economy. Each year the U.S. fishing industry contributes 90 billion dollars and 1.5 million jobs to the U.S. economy. Each species may serve as a predator or prey for another. In this regard, fisheries populations are interconnected and dependent. While humans may depend on these populations as a source of sustenance (food, goods, etc.), humans can also negatively impact population growth. Barriers to migration, pollution, overfishing, and other forms of human-interference may impact spawning patterns of fisheries species. In 2014, 17% of U.S. fisheries were classified as overfished. Therefore, it is necessary to monitor these fisheries populations to determine when policy must be changed in efforts to maintain healthy oceans.

Many groups, including NOAA Fisheries, state agencies, as well as regional fisheries councils and local municipalities, deploy camera and video equipment to monitor fisheries populations. Large amounts of video and photographic data are gathered at timed intervals. However, not all photos contain aquatic life. Currently, employees at these agencies among others are responsible for manually annotating the gathered videos and photos; this means they identify and count the relevant aquatic specimens in the data. Not only is this an inefficient use of time and resources, but also it can lead to inaccurate results due to human error. NOAA Fisheries Management can make a significant improvement in time and resource use through automation of the annotation process.

Table of Contents

• Project Description
• Current Methods
  • Training
  • ML System Design Planning
  • Original Setup
    • Labeling
    • Training
• Quick Start
• Testing
• Pipeline Version Changes
  • Model Enhancement (Version 1)
  • Insights and Optimization (Version 2)
  • Labeling Strategies (Version 3)
  • Augmentation Strategies (Version 4)
  • New Fishery Site Tuning
• Contribution Guidelines

Objectives:

1. Identify fish as River Herring and Not River Herring (or by individual species)
2. Count River Herring Only
3. Count fish passing by in only one direction
4. Date and time stamps
5. Provide summary statistics output tables and figures
6. Provide an estimate of identification and count accuracy
7. Develop software that can be given to fisheries managers so that they can run the software on their own video and have the computer count and total fish for their spawning run season
8. Develop real-time monitoring and counting in the field with weekly downloads
9. Develop a user interface for non-computer programmers
10. Include other monitoring sensor and output such as temperature, current speed, light, and measurements of fish length and biomass would be desirable as well.

Setup

Current setup is a gated entry into the resorvoir in order to allow cameras to capture all of the content in cages.

Cameras and Gates

Camera

• 64mp-ultra-high-res-camera-raspberry
• 950-Analog-Drop-Camera_p_20

Meshtastic

Locations

Currently 4 locations stationed in Massachusetts

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Loading

Herring Example Photos

Methods

• Phase 1 - Detection: During the detection phase we are running our computationally more expensive object tracker to (1) detect if new objects have entered our view, and (2) see if we can find objects that were "lost" during the tracking phase. For each detected object we create or update an object tracker with the new bounding box coordinates. Since our object detector is more computationally expensive we only run this phase once every N frames.
• Phase 2 - Tracking: When we are not in the "detecting" phase we are in the "tracking" phase. For each of our detected objects, we create an object tracker to track the object as it moves around the frame. Our object tracker should be faster and more efficient than the object detector. We’ll continue tracking until we’ve reached the N-th frame and then re-run our object detector. The entire process then repeats.

The benefit of this hybrid approach is that we can apply highly accurate object detection methods without as much of the computational burden.

References

• https://pyimagesearch.com/2018/08/13/opencv-people-counter/
• https://help.ubidots.com/en/articles/1674356-people-counting-systems-with-opencv-python-and-ubidots

Documentation

• Instruction Manual

Dropbox

Currently linking to Dropbox folders for large files

• Original Project Files
• 2017 Software

Whitepapers

• Read more AMS Presentation

Future-Work:

• Continue applying image recognition to herring at different sites
• Research data drift for different levels of lighting.
• Develop graphical user interface for end-users
• Implement automation of fish counting from images that removes manual process of running on individual's machines

Other

• Useful apps and software:
  • Labeling:
    • Roboflow ()
  • Useful Tools and Apps:
    • Testing Threshold: https://kxborg-open-threshold-threshold-a2jyh4.streamlit.app

Model Maintenance and inference:

Development

Setup:

• Python>=3.8 required

Annotation

• Example annotations:

* Example video snippet (click to view):

• Current Methods

  • NOTES:
    • Training:
      • Yolov Medium model improvement of ~30% in speed and 10% performance over original Yolo v5
    • 
    • Training Notes
  • ML System Design Planning

• Original Setup

  • Labeling
    • https://github.com/heartexlabs/labelImg (Used for project)
    • https://www.scalabel.ai
  • Training:
    • Tutorials on training YOLO
      • THIS GUIDE
      • yolov7-training-on-custom-data
    2. Quick Start for this code:
       1. Navigate to darknet folder
       2. Run: *./darknet detector test ../weights/weights_and_config_files/Herring/herring.data ../weights/weights_and_config_files/Herring/herring.cfg ../weights/weights_and_config_files/Herring/herring_final.weights
  • Training:

Alt Text

Testing

1. cd code/tests
2. Run the tests pytest tests code coverage % can be run also: pytest cov=tests tests and showing missing lines: pytest --cov=tests --cov-report term-missing tests

Pipeline Version Changes

1. Model Enhancement (Version 1):

   • Improved inference speed by 40% by updating base code from original research project.
   • Implemented Roboflow for data format transformation and augmentation.

2. Insights and Optimization (Version 2):

   • Identified challenges with cls_loss and optimized model parameters.
   • Leveraged Roboflow's workflow features for efficient dataset maintenance.

3. Labeling Strategies (Version 3):

   • Introduced labeling insights, including the importance of "null" images and maximizing target class in tracking.
   • Applied noise to images for confidence regularization.

4. Augmentation Strategies (Version 4):

   • Adjusted augmenting the box around the class for improved tracking results on golden dataset.
   • Experimented with dropout variations and limited image augmentations for improved tracking results from 22% counting to 15% counting error (MAPE).

5. New Fishery Site Tuning

   • Developed model specifc for other site,\

Contribution Guidelines

We welcome contributions to our project! If you're interested in helping, here's how you can do it:

1. Fork the repository: Start by forking the repository to your own GitHub account.

2. Clone the repository: Clone the forked repository to your local machine to start making changes.

   git clone [email protected]:aus10powell/MIT-Fishery-Counter.git
   

3. Create a new branch: Before making your changes, switch to a new branch. This helps in keeping the main branch stable and simplifies the process of integrating your changes.

   git checkout -b feature-name
   

4. Make your changes: Implement the changes you wish to make. Make sure to keep your code clean and well-documented.

4a. Make sure tests and coverage are updated

5. Commit your changes: Once you're satisfied with your changes, commit them to your branch. Make sure your commit messages are clear and descriptive.

   git commit -am "Add a concise and descriptive commit message"
   

6. Push your changes: Push your changes to your fork on GitHub.

   git push origin feature-name
   

7. Submit a pull request: Go to the original repository on GitHub, and you'll see a prompt to submit a pull request from your new branch. Fill in the details of your pull request, explaining the changes you've made and their impact.

8. Respond to feedback: Once your pull request is submitted, the project maintainers may provide feedback. Be open to making further changes based on their suggestions.

9. Get merged: If your changes are approved, they will be merged into the main project. Congratulations, you've successfully contributed!

Thank you for your contributions!