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Lekha’s Status Report for 02/08/2025

Work Accomplished:

This week, I focused on the initial research and preparation for the necessary hardware components of our projects. Some of my key accomplishments include:

  1. Finalized  to-be-purchased hardware devices pending TA approval
    • NVIDIA Jetson Orin Nano Developer Kit – $249
      • This specific kit offers performance improvements over the base kit, proving useful for AI (object detection tasks). This will prove especially useful for our computer vision capabilities in identifying grocery products.
      • Barrel Jack Power Supply for powering device
    • Arducam IMX477 – $80
      • Real-time object identification for video streams (identify grocery items as it is being placed in cart)
        • 60 FPS
      • Compatible with Jetson Orin Nano
  2. Decided to 3D print case encompassing Jetson Orin Nano and Arducam
    • customizable compact case fit to device sizes
  3. Consolidated research on setting up Jetson Orin Nano and connecting with specific camera 

Progress Status:

I am on track with our current timeline of finalizing device options in order to start preparing device setup for next week.

Next Week’s Goal:

  • Submit purchase forms for each of the hardware devices on Monday with TA approval
  • Mockup of hardware design on prototyping platform in most compact and efficient manner
  • Setup training procedure for YOLOv8 model
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Aanya’s Status Report for 02/08/2025

Work Accomplished:

This week, I focused on the initial research and setup phase of the computer vision pipeline for SmartCart.

  • Started curating a dataset by sourcing existing grocery product images from GitHub repositories and other publicly available datasets.
  • Investigated USFDA food data to map size-to-weight ratios for grocery items, which will be useful for estimating weights when barcode or packaging data is unavailable.
  • Reviewed OCR-based text extraction methods for distinguishing visually similar products by identifying brand names and labels.

Progress Status:

I have laid the groundwork for the data acquisition and preprocessing steps necessary for real-time product recognition. The insights gained this week will guide dataset preparation and the initial implementation of YOLOv8.

Next Week’s Goal:

For next week, I plan to move into dataset preparation and initial model setup by:

  • Finalizing a structured dataset of Aldi grocery products by combining GitHub-sourced images, USFDA data, and web-scraped product images.
  • Implementing OpenCV preprocessing techniques for image enhancement and noise reduction.
  • Setting up a baseline YOLOv8 model with an initial subset of grocery product images.
  • Developing a basic pipeline to match detected objects with database records, using text recognition and visual similarity scoring.