Naoya Yamato

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About

Naoya Yamato is a robotics researcher whose work centers on autonomous visual inspection and change detection for industrial infrastructure. His key research areas include mobile robotics, computer vision, and 3D spatial analysis for plant and pipe inspection systems. Yamato’s major contributions lie in developing methods that enable robots to autonomously detect anomalies by comparing past and current inspection footage. In his most-cited work, "Change Detection in Image Pairs for Plant Inspection Using Mobile Robot" (2025, 5 citations), he proposed a system that leverages pose information to align videos captured during different inspection rounds, allowing precise identification of changes in 3D space. His follow-up study, "Change Detection in Pipe Image Pairs Extracted from Inspection Videos by Sequential Filtering" (2024, 2 citations), extends this approach to pipe surfaces, defining anomalies as deviations from normal states. Though early in his career, Yamato’s work addresses a critical industrial need: automating the detection of structural degradation in hard-to-reach environments. His research promises to reduce human risk and improve maintenance efficiency in plants and refineries, marking him as an emerging voice in field robotics and infrastructure monitoring.