Risks & Management:
- Object Detection Confusion
- Risk
- The camera may struggle to differentiate between similar looking items (e.g. Fiji apples vs Honeycrisp apples) or misidentify certain products.
- Risk management
- Collect & train the YOLOv8 model with wider variety of grocery products to improve detection accuracy. Optimize confidence thresholds in detection.
- Contingency Plan
- Implement a User Confirmation Step: When the app detects similar items, prompt the user with a choice selection (e.g. “Was the item you just put in A, B, or C?”) for quick correction.
- Risk
Design Changes & Justification:
- Initially, we planned to populate our own dataset of Aldi grocery items to account for environmental and lighting discrepancies. However, we plan to limit our scope of the dataset to use the online Aldi database of products to prioritize functionality of our prototype. Now, it should be able to recognize a standard set of grocery items without hindering our progress if we were to spend too much time creating a brand new custom dataset.
Progress:
- App setup & design
- Yolov8 and Open CV pipeline finalization
- Aldi’s dataset collection started
- Finalized to-be-purchased hardware devices
- Designed device shell
