Month: February 2025

The majority of my time this week was spent ensuring the web app’s front end was set up. This involved a considerable amount of research in terms of determining the tools that would be used to build the web app. In setting up the website, I determined that I’d be using Django as the main Python framework for setting up the web app. It utilizes the MVT architectural design, which should provide sufficient capability for the web app. This would also allow for future additions of more interactive features such as controlling and interacting with the robot or counting the number of categorized items in each category(metal, plastic, paper, garbage). I also spent some time experimenting with React but concluded that it is unnecessary for the website’s streaming purposes. I’ve gotten the web app to currently work on local host which is sufficient for our project needs at the moment.

For next week, I intend to complete the web app’s backend capability by having the video streaming component working.

Currently, everything is on schedule.

The majority of time this week was spent on two efforts: verifying that the ±5 pixel expectation of the machine learning model was both not too strict and not to lenient and determining if the Jetson Orin Nano has enough compute for our needs. While evaluating the ±5 pixel expectation, I searched for trash datasets on both Roboflow and Kaggle and eventually settled one on Roboflow that I really liked. After visualizing images from the dataset with their oriented bounding boxes, their centroid, and 5 pixel circle around their centroid, I see that 5 pixels is a robust expectation to have.  Regarding the compute of the Jetson Orin Nano, the specifications say that it has 1.28 GFLOPs and  medium-sized YOLOv8-OBB model needs 208.6 FLOPs.  Even with a FLOP efficiency of 20%, the Jetson Orin Nano should have more than compute to run the model and potential any other assistive processes that strength the centroid calculation process.

Next week, for the first part of the week, I have to investigate a little more time figuring out if fine-tuning existing YOLOv8-OBB models would be better in our use case as opposed to training one from scratch. Moreover, I want to finish preparing the dataset for our use case (e.g. making the background white for the images, making transformations that affect the lighting of the images, and etc.)

Currently, everything is on schedule.