Category: Max Tang’s Status Reports

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.

This week I worked on compiling data for training the walk sign detection model. The model’s performance is only as good as the data that it is trained on, so I felt that it was important to get this step right. I spent a lot of time searching online for datasets of pedestrian traffic lights. However, I encountered significant challenges in finding datasets specific to American pedestrian traffic signals, which typically use a white pedestrian symbol for “Walk” and a red hand for “Don’t Walk.” The majority of publicly available datasets featured Chinese pedestrian signals that use a red pedestrian and green pedestrian symbol, which are not suitable for this model. I decided to instead compile my own dataset by scraping images from Google as well as Google maps. I will also augment this dataset with real world images, which I will begin next week. This progress so far is on schedule, perhaps a little behind. The lack of existing American datasets set my back a little, so I will need to expedite the data collection. Next week I hope to have a fully labeled dataset with multiple angles and lighting situations. This should be ready for model training, which will be the next step in the walk sign detection section.

This week I presented our group’s initial proposal presentation. The presentation went well, and I received many thought-provoking questions that have helped me realize that there were some aspects to our design that we have not considered, such as intersections that have multiple sidewalks. I began searching for suitable models that we can use to create our walk sign image classification model. One of these is an off-the-shelf YOLOv8 model that we can simply fine tune on walk sign images. Another potential solution I found is to gather as many images of walk signs as possible, as a combination of existing online datasets and self-taken images, and upload them to Edge Impulse. Then I can use Edge Impulse’s image classification model, which would be great for our project since Edge Impulse has a feature that lets you create quantized models, which use smaller data types for storing parameters and reduces the total memory required.

Progress is still on schedule. We allocated ourselves a large chunk of time for researching and making the model, and I believe that picking a suitable model at the beginning will help save time tuning and testing later. Next week I hope to be able to start the training and initial testing against validation datasets. This will give ample time for iteration if further improvements are required, which is very likely.