Together with Infranord and Trafikverket, we’ve developed machine learning algorithms for detecting damages on railroad infrastructure.
In Sweden, there are thousands of kilometers of railroads that need to be monitored and maintained. Traditional inspection, where people physically visit the location, is a massive undertaking that requires hundreds of people.
At the same time, delays caused by damaged equipment are a huge problem that costs many millions each year. Clearly, there are opportunities to use machine learning for more effective and automatic quality control. If successful, it’s possible to drastically reduce some of the most severe delays.
The trains must cover 14,000km of electrified railroads. Since the cameras take a new image every 10cm, we get 140,000,000 images. However, there are currently 6 cameras, and the goal is to cover all sections between 1-6 times a year totaling around 2,500,000,000 images every year!
It’s clear that inspection from a distance by people is better than physically visiting each location, but it’s clearly not enough. Luckily, quality control is a perfect application for machine learning.
At first, we didn’t have any real damage images, and finding them manually wasn’t an option. Our initial idea was to do anomaly detection with unsupervised learning. The goal is to teach an algorithm what normal looks like (you can do that in many different ways) and filter out everything that isn’t.
An interesting approach that often difficult in practice, at least on its own. The issue for us is that we have hundreds of anomalies that occur more frequently and look less normal than the damages we want to find. Here are two examples:
The key for us was to create images with fake damages. At first, we used photoshop and made a small dataset for supervised learning. As we found actual damages, we added them to the training data, which, of course, made the algorithm better.
After finding around 20-30 actual damages, we trained a cycleGAN to generate more fake damages, leading to significant improvements. Below are some examples from our GAN.
So far, we've analyzed almost a billion images and found over 200 damages of varying severity. Here are some examples:
We helped our customer to improve their solution for reading text in images and went from 4% error rate to a much better 0.2%.
The goal of this project was to make accesible satellite data easier to work with by removing unecessary objects and by increasing the resolution.
Using new advancements in connecting text and images, we helped a large media production company implement effective video search without the need for meta-data.