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About

Anastasios Doulamis is a leading researcher in computer vision, robotics, and intelligent infrastructure monitoring, with a particular focus on automating the inspection of critical civil structures. His most influential work addresses the pressing need for efficient, non-destructive evaluation of tunnels and bridges. Doulamis pioneered the use of deep learning for automatic crack detection, developing a framework that combines convolutional neural networks with heuristic image post-processing to achieve high-accuracy defect identification. This seminal 2019 paper has garnered over 170 citations, reflecting its impact on transforming manual inspection into a data-driven, scalable process. He further advanced the field by designing an autonomous robotic system for tunnel structural inspection and assessment, integrating mobile robotics with real-time vision analytics—a 2017 work cited nearly 100 times. Beyond these contributions, Doulamis has explored multi-modal sensor fusion and 3D reconstruction for infrastructure health monitoring. His research bridges the gap between academic AI and practical civil engineering, offering cost-effective, safer alternatives to traditional inspection methods. For students and researchers, Doulamis’s work exemplifies how deep learning and robotics can solve real-world challenges in structural health monitoring.