Our team worked together to lay out the use case requirements, scope and schedule of our project for the project proposal presentation. We chatted after the presentation based on some of the questions brought up by other students and the TA. One example was if a student could put a whole tray of objects onto the bin platform. We decided that if we wanted to consider this case in the future, we would have to use detection as well as classification. So we decided to stick with YOLO instead of just Resnet, since resnet would only have the classification part.
This week I did research on the appropriate machine learning model that can be used to perform classification of a plastic bottle. I found a We searched for datasets, and found one that can perform classification of 4 types of drinkable waste. With this dataset I believe we can expand our MVP from only classifying plastic water bottles to also being able to classify milk cartons, glass, and aluminum cans. I started to use some starter code for YOLO to run the code with this dataset I found, and am in the middle of debugging to get the code to run all the way through. For the trapdoor I researched different videos of mechanisms: one would be a servo with an arm that is pulling and pushing the door. The other would be a ledge that retracts to let the door fall open, and an actuator to push the platform back up. Next week hopefully we can start training and also research more specifics of the mechanical aspect of the project.
Drinking waste dataset: https://www.kaggle.com/datasets/arkadiyhacks/drinking-waste-classification