Yingying Huang

Papers

Total Citations

H-Index

About

Yingying Huang is a researcher advancing intelligent automation in critical infrastructure, with a primary focus on the application of machine vision and robotics in hydropower and energy systems. Her most-cited work introduces an intelligent inspection robot designed to overcome the limitations of traditional manual detection in pumped storage stations, particularly for crack and seepage identification. By constructing a convolutional neural network integrated with cross-entropy loss, Huang’s system significantly improves detection accuracy while reducing operational costs and human risk. This contribution addresses a pressing need for reliable, automated monitoring in aging hydropower infrastructure, where early defect detection is essential for safety and efficiency. Though early in her citation trajectory—her top paper currently holds 2 citations—Huang’s work represents a practical convergence of deep learning and field robotics, with clear potential for broader adoption in industrial inspection. Her research sits at the intersection of computer vision, structural health monitoring, and energy system maintenance, offering scalable solutions for the next generation of smart hydropower stations. As the demand for autonomous infrastructure inspection grows, Huang’s contributions are poised to gain increasing recognition.