1. Overview
Our project remains on track, with progress across hardware, computer vision (CV), and mobile development efforts. This week, significant advancements were made in image processing, model prototyping, and mobile app development. While some hardware acquisition was delayed, progress in algorithm development and system integration ensures that we are still aligned with our overall timeline.
2. Key Achievements
Hardware and Embedded Systems
- Conducted initial experimentation with image stitching to assess the feasibility of a single-camera solution.
- Explored OpenCV’s
Stitcher_create() function, identifying key constraints such as the need for image overlap and the drawbacks of high-FOV cameras in feature mapping.
- Gained insights into optimal camera placement, frequency of image capture, and required FOV for effective image processing.
- Prepared for upcoming camera and data transmission pipeline testing once equipment is acquired.
Computer Vision
- Set up the PyTorch environment and implemented an initial YOLOv5 prototype.
- Developed a preprocessing pipeline using OpenCV and conducted preliminary training on small annotated grocery item datasets.
- Started integrating external datasets from Kaggle and Roboflow to improve model accuracy and robustness.
- Began benchmarking inference speed on local (Raspberry Pi) and cloud-based setups to measure latency and performance trade-offs.
Mobile App Development
- Built the foundation of the React Native mobile application, implementing a core feature prototype.
- Configured project dependencies and set up the UI framework for key functionalities.
- Explored integration with backend services to support inventory tracking and recommendation features.
- Prepared for the integration of computer vision capabilities in future iterations.
3. Next Steps
Hardware and Embedded Systems
- Acquire necessary equipment, including the Raspberry Pi, camera, and related components.
- Test camera performance within a fridge environment and optimize settings for optimal image capture.
- Develop and test data transmission pipelines between the camera, Raspberry Pi, and cloud storage (ownCloud).
Computer Vision
- Complete dataset collection and refine annotation processes for improved training quality.
- Conduct further testing on local vs. cloud inference to optimize system efficiency.
- Integrate YOLOv5 model with the mobile application and test its effectiveness in real-world conditions.
Mobile App Development
- Expand the prototype by implementing additional features and refining UI/UX.
- Improve app performance and address any identified compatibility issues.
- Begin integrating computer vision functionalities and explore API options for seamless connectivity.
4. Outlook
The team is making steady progress across all development fronts. While hardware testing was slightly delayed due to equipment requisition timing, the early progress in software-based experimentation ensures we remain aligned with our overall goals. As we move forward, the focus will shift toward system integration, testing, and optimization to create a fully functional prototype in the coming weeks.
Part A: Public Health, Safety, and Welfare (Will)
Our fridge camera system enhances public health by promoting better nutrition and reducing food waste. By providing real-time tracking of fridge contents, users can make more informed decisions about meal planning, ensuring they consume fresh and balanced meals. The system also helps prevent the consumption of expired or spoiled food, reducing the risk of foodborne illnesses. Additionally, by suggesting recipes based on available ingredients, the product encourages healthier eating habits by making meal preparation more convenient and accessible.
In terms of safety, the automated rail system is designed with smooth and secure movements to prevent accidents such as knocking over or damaging fridge items. The device is built with user-friendly controls and safety mechanisms to ensure it does not pose a hazard to household members. The system also enhances welfare by addressing basic food security concerns—by reducing food waste and maximizing ingredient use, households can make their groceries last longer, ultimately benefiting those on tight budgets or with limited access to fresh food.
Part B: Social Considerations (Steven)
Socially, this product addresses the needs of diverse households, including busy professionals, families, and individuals who struggle with meal planning. For families, the ability to track fridge contents remotely ensures better coordination in grocery shopping, preventing unnecessary purchases and reducing food waste. The recipe recommendation feature also helps bring people together by facilitating home-cooked meals, fostering stronger family and social bonds.
For individuals with dietary restrictions or cultural food preferences, the app can be tailored to suggest recipes that align with their specific needs, promoting inclusivity and personalization. Additionally, the product encourages sustainability by helping consumers become more mindful of their consumption habits, aligning with broader social movements that advocate for reducing food waste and promoting environmentally responsible living.
Part C: Economic Considerations (Jun Wei)
From an economic perspective, the fridge camera system helps households save money by reducing food waste and unnecessary grocery purchases. By keeping an up-to-date inventory of fridge contents, users can avoid buying duplicate items and make more efficient use of what they already have. The recipe recommendation feature further enhances economic efficiency by helping users maximize ingredients, minimizing waste, and stretching grocery budgets further.
On a larger scale, this product could contribute to economic benefits in the food industry by supporting more efficient consumption patterns, potentially reducing demand volatility in grocery supply chains. Additionally, the integration with an iPhone app presents opportunities for monetization through premium features, such as AI-driven meal planning, partnerships with grocery delivery services, or integrations with smart home ecosystems. As adoption grows, this product has the potential to create job opportunities in software development, hardware manufacturing, and customer support, contributing to economic activity in the tech and consumer goods industries.
