Over the last two weeks, I began training YOLOv8 on one of the online airport datasets. I also realized the need for Optical Character Recognition (to interpret words and numbers in addition to arrows) and delved into ways to implement and integrate it into the software subsystem. By next week, I hope to have a functional YOLO model for our purposes and robust implementation plans for OpenCV and OCR.
Team Status Report 02/22
We have decided on the form factor of the device: waist mounted camera system. We have started training the ML model, the CAD of the physical product and UI components. Everyone is working individually right now with a plan to integrate soon.
Team Status Report 2/15
In terms of safety, GateGuard mitigates risks associated with getting lost, and accidental trespassing in restricted areas. Traditional methods, such as relying on assistance from airport staff, can be inconsistent and unreliable. In terms of social factors, we are making sure that GateGuard is as inconspicuous as possible for visual appeal. We are also making sure that the device promotes independent mobility, which can inspire confidence to our users. In terms of economics, we have to make sure that for the users, the device provides a cost-effective alternative to other methods, such as hiring a travel assistant.
Team Status Report 2/8
As a team we’ve narrowed our scope so that it’s just navigating from after security to the boarding gate. We feel that before security there’s too many variables in airport layouts, signs and procedures. We realize that’s an important part of the process, however, it might be too much to tackle in our time frame.
We’ve also created a bill of materials. We went through the ECE inventory and requested a depth camera and a raspberry pi 4. We chose this over the jetson since it’s lighter and requires less power.