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Team Status Report for 10/5

This week, we presented our design review on Monday and started working on our design report. 

One risk that we are currently facing is the YOLOv7’s ability to recognize recyclable objects. While we haven’t started training the model yet, we realize that there are many different possible recyclable objects that we have to take into consideration, especially for types of paper. We are mitigating this risk by finding a dataset that covers all of the different types of materials that we need to recognize.

After doing more research into display screens, we realized that the display screens that we wanted to use were very small, and may not be able to fit all of the information. Instead, we plan to use multiple displays instead and piece them together, as we still think that this specific display screen is the most compatible with the rest of our project. This would not incur a lot of new costs, as the screens that we originally wanted to purchase came with multiple screens, and the only extra costs would come from wires to connect the extra screen.

There have been no changes made to the schedule this week. We are on time and wrapping up the research that each of us have been doing. Next week, as soon as we receive all the parts that we ordered, we plan to start building our hardware components and test the CV.

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Justin’s Status Report for 10/5

I spent time in the beginning of the week working on the design presentation, which went well, and we got some useful feedback. I have landed on a suitable dataset for our project: https://www.kaggle.com/datasets/arkadiyhacks/drinking-waste-classification. This dataset not only contains thousands of images to train on, but the images show the images as they might appear when captured by our camera (ie. the item is photographed from above), rather than a stock image. Unfortunately, I was out of Pittsburgh in the second half of the week to attend a wedding, so I was not able work with the Jetson and camera (which arrived after I left), so next week I am hoping to hook up the camera to the Jetson, load YOLO (off the shelf), and test out that setup. I will also start setting things up to train YOLO with a custom dataset. Luckily YOLO has many resources online, I’ve already watched some videos, and it should hopefully be easy to set up. I will also be working with my team on the design review. Being away from Pittsburgh has delayed my progress beyond what I initially hoped, and I anticipate the design report and midterms to take time next week, so I am also planning to work on this project over fall break, bringing the camera and Jetson back home with me if necessary (I should not need any other hardware to work on the CV).