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Team Status Report for Apr 12 2025

Team Status Report

Progress This Week

Our team made meaningful progress across hardware, cloud integration, and software components in preparation for the next stage of development.

  • Computer Vision & Cloud Integration: Steven worked on integrating the CV model and cloud components of the system. He successfully tested image upload and retrieval through the cloud pipeline and began preliminary integration of the YOLOv5 model for live inference. Training of the model is ongoing, and initial tests on the cloud system have been promising.

  • Hardware & Imaging: Jun Wei completed the construction and integration of the motorized camera slider with the updated camera hardware. The system has been mounted onto a smaller breadboard and tested within the mini fridge enclosure. He is now moving toward integrating the image stitching script with the Raspberry Pi system and preparing for cloud pipeline integration in collaboration with Steven.

  • Mobile Application & Recommendation System: Will implemented Google OAuth in the mobile app, enabling secure user sign-in and laying the groundwork for personalized features. He also started building the recipe recommendation system using LangChain, setting up the initial framework and exploring prompt designs to map fridge contents to relevant recipes.

Plans for Next Week

  • Finalize deployment of the YOLOv5 model for cloud inference and continue refining the cloud data pipeline (Steven).

  • Integrate image stitching into the Raspberry Pi workflow and begin transmission testing (Jun Wei).

  • Continue development of the recipe recommendation system and begin linking it with user preferences stored post-login (Will).

Overall, the team is on track with project milestones. Core components across hardware, CV, and app infrastructure are coming together, and collaboration between sub-teams is increasing as we begin cross-component integration.

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Jun Wei’s Status Report for Apr 12 2025

1. Personal accomplishments for the week

1.1 Motorized camera system integration

This week, I completed construction of the motorized camera slider and integration with the new camera obtained. I found a way to tether the movement of the timing belt to the camera baseplate using zip ties. I have also managed to move the system to a smaller breadboard and have tried placing it within the mini fridge that we will be using.

2. Progress status

I am on schedule as the motorized camera slider has been integrated with the camera system. I am now working on integrating the stitching algorithm script with the overall Raspberry Pi script.

3. Goals for the upcoming week

  • Integrate stitching algorithm
  • Integrate with cloud transmission pipeline (to work with Steven on this)
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William’s Status Report for Apr 12 2025

This week, I implemented Google OAuth integration into the mobile application, allowing users to sign in seamlessly and securely. This sets the foundation for saving personalized preferences tied to user accounts.

I also began working on the recipe recommendation system using LangChain. Initial efforts were focused on setting up the pipeline and experimenting with different prompt structures to connect user inputs with relevant recipe suggestions.

Plans for Next Week

  • Finish building out the recipe recommendation system and fine-tuning its performance.

  • Start linking user preferences to recipe suggestions post-login.

  • Explore potential improvements to the UI for the Preferences and Recipes tabs.

Progress this week has been steady, with key backend components starting to come together alongside the app frontend.

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Steven’s Status Report for Apr 12 2025

For this week, I worked on integrating the CV and cloud components of our system. I tested image upload and retrieval through the cloud data pipeline, as well as preliminary integration of the YOLOv5 model onto the cloud system for live inference testing.  I’ve also continued further training of our YOLOv5 model.

I am currently on track with our milestones. I am working on integrating our model with our cloud pipeline which will also be integrated with the Raspberry Pi.

For next week, I plan to finalize the deployment of the YOLOv5 model onto the cloud and run cloud inference on fridge images. Furthermore, I plan to continue refining the data upload pipeline and explore backup storage solutions in the case of a cloud failure.

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Steven’s Status Report for Mar 23 2025

For this week, I continued with refining the training pipeline for the object detection model, in order to maximize our performance for the demo . I expanded our dataset with the Open Images database, filtering out food-related classes to our use case, significantly increasing our existing data. Furthermore, I am looking to experiment with the YOLOv10 model in order to further increase accuracy.

I am currently on track with our milestones. Preliminary training have been completed, and we are working to maximize our accuracy and finalize our model. I am also working on deploying our model onto the Raspberry Pi.

For next week, we aim to proceed with our demo, during which I aim to show our model with the optimal accuracy. I will continue working on integrating our model with the tentative pipeline for fridge item detection, and continue expanding our training dataset and tuning our parameters to optimize accuracy.

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Team Status Report for Mar 23 2025

1. Overview

Our team made significant progress across all of the project components, with our main focus being preparing for the midterm demo. We refined our computer vision model, made significant progress on our motorized camera slider subsystem, and refined the user interface and user experience for our mobile application. Our challenges include difficulties of using our existing cameras and reliability issues with our cloud data pipeline.

2. Key Achievements

Hardware and Embedded Systems
  • We made progress on the motorized camera slider subsystem, constructing the slider and powering it with a Nema 17 stepper motor integrated with a Raspberry Pi
  • Completed calibration, optimized translation speed and stepping requirements for full traversal
  • Integrated ring light system to ensure consistent lighting throughout scan
Computer Vision
  • Refined the training pipeline for the object detection model, maximizing performance for demo
  • Completed preliminary training, working on optimizing hyperparameters and training sequence and maximizing accuracy
Mobile App Development
  • Improved user interface and user experience of our mobile app
  • Implemented several UI improvements to ensure smooth and visually coherent experience for demo purposes

3. Next Steps

Hardware and Embedded Systems
  • Add stable baseplate mount to slider to improve stability
  • Integrate camera system with slider and complete full system testing
Computer Vision
  • Continue expanding dataset and tuning hyperparameters to optimize accuracy
  • Integrate model with pipeline for fridge item detection
Mobile App Development
  • Implement recommendation system
  • Continue backend integration to improve data storage and transmission

4. Outlook

Our team is on track despite minor delays, and will present functioning demos showcasing hardware control as well as user interface. We will continue working on feature development and tighter integration between our hardware and software systems.

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Jun Wei’s Status Report for Mar 23 2025

1. Personal accomplishments for the week

1.1 Motorized camera slider and lighting

This week, I constructed the motorized camera slider and integrated the ring light. The slider uses a Nema 17 stepper motor that interfaces with the Raspberry Pi via a A4988 driver. I managed to calibrate the motor and found the optimum translation speeds as well as number of steps required to traverse the entire slider. Triggering a scan causes the motor to traverse and stop multiple times along the length of slider for the camera to capture photos for stitching. At the same time, the ring light activates until the scan is complete.

Motorized camera slider
Ring light system

2. Progress status

I am slightly behind schedule as the motorized camera slider has not been integrated with the camera system yet.  This is because the appropriate camera (ordered 2 weeks ago) has yet to arrive. The system also needs to be moved to a smaller breadboard once fully integrated. The cloud data transmission pipeline developed, while functional, still presents some reliability issues.

3. Goals for the upcoming week

  • Add stable platform/baseplate mount to the camera slider
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William’s Status Report for Mar 23 2025

Progress This Week

This week, I focused on working with my teammates in preparation for our midterm demo. I helped out where needed and spent some time polishing the mobile application’s UI to improve the overall look and feel. My efforts were mainly geared toward refining existing components and making final adjustments to enhance the user experience for the demo.

Plans for Next Week

  • Resume development on new features for the mobile app.

  • Revisit the computer vision integration research and begin narrowing down options.

  • Start backend integration for better data handling and storage efficiency.

This week was centered around ensuring a solid demo presentation, and I’m looking forward to picking up development momentum again in the coming days.

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William’s Status Report for Mar 16 2025

This week, I focused primarily on maintaining the stability of the mobile application. While there weren’t any major updates or feature additions, I revisited portions of the codebase to ensure consistency and keep things organized for future development.

I continued to read through documentation for a few computer vision libraries but haven’t yet committed to a specific approach or tool. Similarly, backend progress was limited—most of my work involved reviewing previous plans and considering potential next steps for data storage and handling.

Plans for Next Week

  • Make incremental UI/UX refinements based on earlier feedback.

  • Narrow down a shortlist of viable computer vision tools to begin experimenting with.

  • Begin small backend updates focused on structuring data handling logic.

Overall, this was a lighter week in terms of visible progress, but it provided a useful opportunity to regroup and prepare for more development work moving forward.

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Jun Wei’s Status Report for Mar 16 2025

1. Personal accomplishments for the week

1.1 Data transmission pipeline

This week, I continued debugging the data transmission pipeline on the Raspberry Pi that uses the boto3 Python package. The pipeline is able to reliably send files over, however, there appear to be latency issues with the time taken for transmission. I will have to investigate resizing the images before transmission.

2. Progress status

I am slightly behind schedule I have not started work on the motorized camera system construction. The data transmission pipeline developed, while functional, still presents some reliability issues.

3. Goals for the upcoming week

  • Begin construction of motorized camera slider