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Christine’s Status Report for 3/1

Progress

  • Design Report Completed: Finalized and refined the system design after conducting additional AWS research. Key architectural changes were made based on our findings (see the team status report for details).
  • Updated Central Server Block Diagram: The design now reflects the latest refinements, ensuring clarity in system architecture. 
  • CI/CD Pipeline Implemented: Initially, I was manually setting up AWS services, but as the project grew, it became unsustainable. To streamline deployment, I set up a repository (Github Repo) with CI/CD (PR), which now automates testing and deployment. The folder structure is modular and designed for maintainability.
  • Watchlist Query Layer Implemented: A basic version (PR1 + PR2) is now complete and fully tested with manual tests + jest tests, though it currently operates without message queuing. The system generates and returns a pre-signed URL for secure data access.

In Progress

  • Web App Access Layer (Watchlist Management): Currently working on implementing this to allow the web app backend to post updates to the global watchlist. I’ve completed a basic implementation and tested it manually (draft PR).  I plan to add a Jest test suite for automated testing. My goal is to wrap this up as soon as possible so the web app can begin integrating with it.

Next Steps

  • Set Up Web App Backend Architecture: Set up a basic backend folder structure (controller, model, db, env) and implement a basic authentication endpoint to enable user login. Once completed, Andy will be able to take over further web app backend tasks.

Overall Status

  • The project remains on track. While the workload is demanding, the transition from manual AWS setup to automated CI/CD has significantly improved efficiency. Now, my focus is on completing the web app integration to ensure smooth interaction with the backend.
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Vicky’s Status Report for 3/1

Personal Accomplishments

  • Design Report:
    • Wrote and edited the design report
  • ML License Plate OCR:
    • Cleaned up platesmania.com dataset through script and manual inspection to improve training quality
    • Benchmarked a variety of OCR models and selected en_PP-OCRv3_rec model for its ease of integration with Python and lesser likelihood to overfit (93% accuracy onplatesmania.com 80% synthetic + 20% real-world license plate dataset, 84% accuracy on platesmania.com 100% real-world license plate dataset)
  • ML End-To-End:
    • Designed, implemented, and tested the end-to-end script, achieving 81% end-to-end accuracy
  • Dash Cam Bringup & Testing:
    • Collaborated with Andy to bringup and test the RPi 5 board and camera module 3

Progress

My progress is on schedule.

Schedule

  • Single-board computer bringup and testing
  • Camera module bringup and testing
  • GPS module bringup and testing
  • Network module bringup and testing