Maintenance of train track equipment is cumbersome and inefficient. Plans showing the location of equipment are always out of date and the information is usually only available in PDF form. Because of this, technicians have a hard time locating the equipment that needs to be repaired.
What it does
In our solution, a camera is installed in the driver's cabin on a train. We built a neural network that analyzes the camera feed to tag all equipment on the captured route and correlates the tags with equipment lists. The merged data is then displayed on a Google Map so the technician can easily find the equipment their looking for and plan the quickest route to get there.
How we built it
Challenges we ran into
Getting the YOLOv3 network to run on our remote server required resolving many dependencies. Choosing the correct training configuration required many iterations.
Accomplishments that we're proud of
The performance of the recurrent convolutional neural network, given the small training set and short training time, is surprisingly good. We've managed to match all equipment with objects found in the image dataset.
What we learned
We've learned how to quickly choose the right neural network and then build, train and deploy it in a project.
What's next for YOLOTrain
To further assist technicians in the field, we would want to develop a mobile augmented reality app. When arriving to a track on foot, the app will show the technician on a camera overlay the direction and distance of the closest signals and balises.