Comparing two images and detecting the differences is something that computers excel at; with a stationary camera they notice changes much faster than humans. Given the plethora of cameras already installed in South Florida buildings, a system could monitor these feeds and detect any change in the building itself. The only challenge in such a system is the ability to distinguish between the concrete wall of a building and cars or people passing by, but that is easily doable with modern technology.
This idea — change detection — is an important concept for an AI model. Even if a concrete structure doesn’t display any signs of damage, it shouldn’t move. The solution is straightforward — a camera watches a wall, and if the wall changes in any way, people should be alerted.
One thing I knew from living in Miami Beach, is that every high-rise in South Florida already has cameras everywhere — even in the parking garages.
I’ve been able to train a basic model to detect damaged concrete in very little time. A number of their engineers have been extremely supportive and interested in this project, and worked with me to create the working prototype. I was able to put together a training data set of 300 photos of damaged concrete, and 173 photos of undamaged concrete. This is a rather small data set for a project of this scope, but it was sufficient to build a fairly accurate prototype.
The need for quality training data is frequently a blocking issue in AI. If you’ve ever used facial recognition on Facebook or Google, you’ll probably have noticed that it becomes more accurate over time, as you let it know when it’s correctly tagged your face and when it hasn’t.
The same is true for images of “concrete cancer” and “concrete spalling.” By far the most time consuming part of creating the prototype detector model seen in the video was the collection of training data. The more images I can get of both damaged and undamaged concrete, the better the model will become. If anyone has access to large numbers of photos of damaged concrete and willing to share, let me know! The actual training of the model itself was accomplished overnight while I slept.
My prototype model works fine with the footage of the Champlain Towers’ garage. All that’s left is to try it out on a live camera feed, and testing a system that could save lives in the future.
Continue reading: https://towardsdatascience.com/how-a-i-can-prevent-future-building-collapses-before-they-happen-71c3bf3740b5?source=rss—-7f60cf5620c9—4