Xianyin Mao

China Southern Power Grid (China), Guizhou Electric Power Design and Research Institute

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About

Xianyin Mao is a leading researcher in intelligent inspection systems for electrical power infrastructure, with a focus on multimodal imaging, robotics, and deep learning. His work addresses critical challenges in automating the monitoring and maintenance of substations and power transmission lines. Mao’s most impactful contribution is a novel image registration method for visible and infrared images, leveraging quadrilateral features to enable electrical equipment inspection robots to fuse data from different sensors—a foundational technique cited 9 times. He further advanced the field by developing a BIM-based 3-D multimodal reconstruction framework for substation equipment inspection images (2024, 7 citations), which reduces the workload of processing vast 2-D inspection data. Mao has also tackled obstacle detection for power line inspection robots using deep learning (2019), improving safety in complex mountain environments, and designed an insulation skin wrapping robot for overhead distribution lines (2024) to prevent contact hazards. With a career spanning from foundational registration algorithms to applied robotics, Mao’s work has directly enhanced the reliability and efficiency of power grid inspections, earning recognition for integrating computer vision, robotics, and practical engineering solutions.