Check-in #2 Introduction (from proposal): Bridges and skyways are common components of our transportation system. To ensure they are in sound structural condition is critical for safety. Structure health monitoring(SHM) is a system to measure and track the health of bridges. Although it has been developed for decades, it is hard to popularize due to the expensive equipment. We introduce a more approachable method to estimate the live loads of bridges based on recognizing the quantity and categories of vehicles on the bridge. With the aim to estimate the live loads of a bridge or a skyway, we set up two traffic cameras on both sides of Wang Village Bridge to collect raw video data. We label the video data to create our own training/developing/testing dataset. Then we use computer vision techniques based on the Yolo4 model to identify the categories of vehicles.
Challenges: So far we have had some difficulties with communicating how we are going to work on the project, especially since we are in different time zones. This has made coordinating our efforts a bit difficult, but this should get easier as we have more time to dedicate to the project.
Insights: At this point in time we do not have concrete results to show.
Plan: We need to dedicate more time to setting up the architecture of the model itself so we can get to a point where we can run it and achieve results. Going forward we will be able to focus more time, allowing us to work on setting up and tuning our model architecture.
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