This week I finished collecting all of the pedestrian traffic light data and also began the process of training the YOLOv8 image classification model. I explored collecting data through different ways but ultimately gathered most of my images from Google Earth. I took screenshots at various intersections in Pittsburgh and I varied the zoom distance and angle of each traffic light to get a diverse dataset. I also made sure to find different environmental conditions such as sunny intersections versus shadier intersections. Initially I explored other ways of collecting data such as taking pictures with my phone, but this proved to be too inefficient, and it was too difficult to get different weather conditions and going to different intersections with different background settings (buildings vs. nature) was too hard. I also explored using generative AI to produce images but the models I tried were unable to create realistic images. I’m sure there are models capable of doing so, but I decided against this route. I also found a few images from existing datasets that I added to my dataset.
The next step was to label and process my data. This involved categorizing each image as either “stop” or “go”, which was done manually. The next step was to prepare it for the YOLOv8 model, which involved putting bounding boxes around each pedestrian traffic light box in each image. I did this using Roboflow, a web application that let me easily add bounding boxes and export it in a format that can be directly inputted into YOLOv8. Then it was simply a matter of installing YOLOv8 and running it in a Jupyter Notebook.
Progress was slightly behind due to the initial difficulties with data collection, but I had updated my Gantt chart to reflect this and am on schedule now. Next week I plan on tuning the YOLOv8 model to try and increase the accuracy on my validation dataset, which so far needs improvement.