1. Overview
Our project is on schedule across hardware, computer vision (CV), and mobile development efforts.
2. Key Achievements
- Hardware and Embedded:
- Selected the IMX219-160 camera for its wide FOV, low cost, and easy Raspberry Pi integration.
- Planned a DIY motorized camera slider, ensuring custom control and reduced integration challenges.
- Computer Vision:
- Chose OpenCV for image preprocessing and YOLOv5 for object detection, balancing real-time performance and accuracy.
- Preparing a dataset of fridge items for initial model training and testing.
- Mobile App:
- Evaluated multiple development options, decided on React Native due to robust community support and cross-platform benefits.
- Outlined a prototype focusing on core inventory features and system integration.
3. Next Steps
- Hardware and Embedded:
- Purchase and test the camera, LED ring light, and slider components in a fridge setup.
- Start assembly of the DIY slider and interface it with the Raspberry Pi.
- Computer Vision:
- Finalize dataset collection and begin training YOLOv5 on a sample set.
- Explore and benchmark local vs. cloud inference options for efficiency and scalability.
- Mobile App:
- Implement a basic React Native prototype with essential UI elements and navigation.
- Integrate data retrieval and display features, testing on both iOS and Android.
4. Outlook
The team is on schedule with our project. We will continue refining each component to ensure seamless integration and a functional system in the upcoming weeks.