Simon Stent

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About

Simon Stent is a leading researcher in computer vision and robotics, with a primary focus on automated visual inspection and change detection for critical infrastructure. His work addresses the challenge of enabling safe, objective, and efficient monitoring of large-scale structures, particularly tunnels. Stent’s major contributions include developing a low-cost robotic system that captures high-resolution images and processes them into detailed maps of tunnel linings, making visual inspection more accessible and reliable. His most cited paper, "Detecting Change for Multi-View, Long-Term Surface Inspection" (2015, 54 citations), presents a novel system that uses structure-from-motion to build panoramas and register images from different time points, reliably detecting subtle changes like hairline cracks. This work has significant implications for preventive maintenance and safety. With a total of 72 citations across his top papers, Stent’s research bridges robotics and computer vision to solve real-world problems, offering practical tools for infrastructure monitoring. His achievements highlight the potential of low-cost automation to transform traditional inspection practices, making his work essential reading for students and researchers interested in applied computer vision and field robotics.