Author: mhtang

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.