CrossTech Cuts High-Risk Rail Faults 96% with Computer Vision Predictive Maintenance on Google Cloud
UK startup CrossTech built Hubble, an AI computer vision platform that analyzes video captured from trains to detect hazards such as overgrown vegetation, signal obstructions and track ballast issues, running on Google Cloud's Compute Engine, Cloud Run and Vertex AI. Using Vertex AI, CrossTech halved the time to build a first model version to six weeks and cut deployment cycles by 70%. The predictive maintenance has delivered roughly £20 million in annual net maintenance efficiency for customers, reduced unplanned service interruptions by 30%, cut high-risk faults on key lines by 96%, and enabled frontline teams to remediate 6,000 faults.
Overview
UK startup CrossTech built Hubble, an AI computer vision platform that analyzes video captured from trains to detect hazards such as overgrown vegetation, signal obstructions and track ballast issues, running on Google Cloud's Compute Engine, Cloud Run and Vertex AI. Using Vertex AI, CrossTech halved the time to build a first model version to six weeks and cut deployment cycles by 70%. The predictive maintenance has delivered roughly £20 million in annual net maintenance efficiency for customers, reduced unplanned service interruptions by 30%, cut high-risk faults on key lines by 96%, and enabled frontline teams to remediate 6,000 faults.
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Inspect the highlighted sourceThe challenge
Traditional rail and road infrastructure inspections are time-consuming and costly, requiring inspectors to manually inspect sections of track for safety and reliability issues; modern lidar detection systems can save time but are expensive and complex to administer.
The solution
CrossTech built Hubble, an AI computer vision platform that analyzes video data captured from trains to proactively identify hazards like overgrown vegetation, signal obstructions, level crossing sighting risks and track ballast issues, running on a containerized microservices architecture using Compute Engine and Cloud Run that auto-scales with demand, and using Vertex AI (including Vertex AI Notebooks) to speed up model development and App Engine to automate deployment.
Reported business value
CrossTech halved the time to build a first model version to six weeks using Vertex AI and cut deployment cycles by 70%; its predictive maintenance has contributed to approximately £20 million per annum in net maintenance efficiency, reduced unplanned service interruptions by 30%, cut high-risk faults on key lines by 96%, and helped frontline teams remediate 6,000 faults.
Sources
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