Team Status Report for 03/16/2024

This week was a productive week for our team.  We have started training our model, made good progress in continuing to test and calibrate our ultrasonic sensors and connecting them to RPi and our Jetson, and also have started to work on our audio messages for our iOS app.

We ran into a small risk this week. While we were working on our audio messages, we realized that there might be a small compatibility issue for the text-to-speech for iOS 17. Switching to iOS 16 seems to have resolved the issue for the moment, but we will test extensively to ensure that this will not become an issue again.

The schedule has remained the same, and no design changes were made.

Ishan’s Status Report for 03/09/2024

This week a large portion of my time was spent working on the design document and report. I worked mainly on the system architecture, design tradeoffs, and system implementations where I made sure to detail the exact specs of our device as well as the other options we considered for different hardware components of our device. Beyond the design report, I ordered the Jetson and worked on setting up the Jetson as well as connecting it to the Raspberry Pi. Furthermore, I completed research on our prioritization algorithm and now have an outlook on how we’ll be filtering the data received by the ultrasonic sensors.

My progress is on schedule.

Next week, I hope to implement my filtering algorithm as well as make sure all the components of the device: sensors, RPi, and Jetson function as desired together.

Team Status Report for 03/09/2024

We haven’t run into any more risks as of this week.

One change we made was the program we are using to run the object detection ML model from YOLO v4 to YOLO v7-tiny. We have opted for this change in the model as the YOLO v7 model reduces computation and thus will reduce latency in the object detection model. Moreover,  the program works at a higher frame rate making it more accurate than the YOLO v4 model for object detection. Additionally, this model is more compatible with the RPi while maintaining a high accuracy. We haven’t incurred any costs as a result of this change, but we have benefited through lower latency and computation.

The schedule has remained the same.

 

A was written by Ryan, B was written by Oi and C was written by Ishan.

Part A:

When considering our product in a global context, our product hopes to bridge the gap in the ease of livelihood for people who are visually impaired compared to people who are not. Since 89% of visually impaired people live in low/middle income countries with over 62% in Asia, our product should significantly also help close the gap among the visually impaired community. With our goal to make our product affordable and function independently without the need for another human, we hope to help people in lower income countries travel easier, allowing them to accomplish more. In addition, as we develop our product we hope to help people travel to other countries as well (ie. navigating airport and flights) significantly increasing the opportunities for visually impaired people globally.

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5820628/#:~:text=89%25%20of%20visually%20impaired%20people,East%20Asia%20(24%20million).

 

Part B:

There are many ways that our product solution is meeting specific needs with consideration of cultural factors. Many cultures place a high value on community support, inclusivity, and supporting those with disabilities. By helping the visually impaired navigate more independently, we are aligning with these values and fostering a more inclusive society. Next, there are some societies that have strong traditions of technological innovation and support for disability rights. Our product is a continuation of this tradition, where we use the latest technology to better social welfare. We will also be using the third most spoken language in the world, English, to provide voice over guidance to our users (https://www.babbel.com/en/magazine/the-10-most-spoken-languages-in-the-world). 

 

Part C:

When considering environmental factors, there are several different ways our product meets needs considering environmental factors. Our product can take into account extremities in the environment like fog or something that would make the camera quality of our device blurry by running our ML model on photos with different lighting and degrees of visibility.  Additionally, our device can enable visually impaired people to travel independently meaning that there’s less reliance on other modes of transport and other resources that could potentially damage the environment. Our device promotes and enables walking as a mode of transport, meaning less use of other modes of transport like cars that potentially damages the environment.

Ryan’s Status Report for 03/09/2024

This week, I worked with my team to finish up the design report. As a result of some nee research, I also switched to the YOLO v7-tiny architecture instead of the YOLO v4 architecture for the obstacle detection model. I have mostly completed the code for the new architecture, but still have a few bugs to work out. I have also finalized on using Microsoft’s Common Objects in Context Dataset, and have collected the labelled images for training, validation, and testing.

My progress is sightly behind schedule as of this week because of the change in the network architecture, but I hope to use the upcoming slack week to get back on schedule.

Next week, I hope to train a small model using a part of the collected images and hope to have some results. I will also finalize the camera module and place the order and hope to start preparing for our demo.

Oi’s Status Report for 3/9/2024

What did you personally accomplish this week on the project? Give files orphotos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

I personally focused most of this week doing the design document. I helped the team get started as well as created diagrams for the design document.  I also gathered feedback from Eshita via Slack on that to incorporate into our final design paper. I also looked into creating voice over onto screens in iOS and started watching youtube videos on how to integrate that into the system for once I get back from break!

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

I believe that my project is on schedule.

What deliverables do you hope to complete in the next week?

I hope to finish learning how to create voice over onto screens into iOS and integrate that onto the screens. I also hope to learn how to create specific voice overs to read over objects discovered.