The new system has been developed after two and a half years in a Knowledge Transfer Partnership between Omnicom Balfour Beatty and the University of York, supported by Innovate UK.
The two organisations shared academic understanding and practical, industry-led insights to develop machine-learning technology to digitalise and advance the way in which railway line inspections are carried out.
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A camera attached to the front of the train moves along rail tracks in need of inspection. It captures high-definition images of the rail track to generate data which is then analysed to highlight inaccuracies and faults on the tracks.
The technology also assists in identifying where faults may occur, allowing preventative fixes to be implemented as opposed to urgent repairs after an issue arises.
The automated technology, which is currently being progressed from proof of concept into a commercial grade software, will be more efficient and safer than the manual track inspection process, Balfour Beatty Omnicom claimed.
Stephen Tait, head of operations for Omnicom Balfour Beatty and project lead, said: “We are developing digital technologies that are rapidly changing our industry; from ‘predict and prevent’ technology and advanced digital surveying techniques through to data science. All of our solutions are underpinned by a long legacy of design and construction expertise.
“Our collaboration with the University of York has been invaluable; this latest innovation is an excellent example of how Balfour Beatty continues to deliver our commitment to reduce our onsite work by 25% by 2025 as we progress against our commitment to develop technologies to evolve the digital railway for a more reliable, cost efficient and safe network for all users”
Professor Richard Wilson, lead researcher on the project from the Department of Computer Science at the University of York, said: “These machine vision technologies for high speed rail inspection will improve the reliability of the railway network, reduce costs and increase the safety of manual inspection. The computer vision and machine learning technologies provide automated inspection of complex assets such as junctions and crossings”.
