Abstract: The Netherlands have the most heavily used railway network in Europe. Every day, travellers make 1.1 million trips by train, 152 million train kilometres in total. This indicates the importance of efficient and thoughtful usage of the railway infrastructure. By monitoring and inspecting the metal railway tracks and the switches, early stage detection of defects is possible, allowing cheaper and timely interventions. This does not only increase the safety level, but also the availability of the heavily used tracks.
Inspectation and CQM have developed an image processing solution for the automatic inspection of railway tracks. In the old days, inspectors had to walk over the tracks to inspect them, which was not without danger. Today, specially equipped trains with cameras make detailed pictures of the railway tracks. This gives us a huge amount of imagery, which has to be looked at by the same inspectors. CQM and Inspectation drastically improve this process using Deep Learning techniques, building a solution for automatic detection of possible defects saving the inspectors a lot of time and allowing cheaper and more frequent railway inspections. The project was awarded the Hendrik Lorentz Prize at the 'Nederlandse Data Science Prijzen' of the Koninklijke Hollandsche Maatschappij der Wetenschappen (KHMW) en Big Data Alliance (BDA).
This talk will be about the practical application, a little bit of the mathematics behind the deep learning model, how to make all of this actually work in practice, and about how to gain trust in the model, not purely using it as a black-box model. But most of all, in this talk, I aim to show that deep learning models are not totally new, maybe scary IA methods, but closely related to methods and models that we as operations researchers, computer scientists and statisticians are familiar with.