Angie Wu

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Total Citations

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About

Angie Wu’s research lies at the critical intersection of civil infrastructure, robotics, and deep learning, with a focused mission to modernize the inspection and maintenance of urban water distribution networks. Her work addresses the pressing challenge of detecting and assessing underground utilities, which are vital to city infrastructure yet prone to deterioration from aging and high demand. Wu’s major contribution is the development of autonomous systems for in-pipe inspection robots, specifically pioneering methods for valve detection—a key component for pipeline control and repair. Her most cited paper, “Valve Detection for Autonomous Water Pipeline Inspection Platform” (2021), has garnered 29 citations, establishing a foundational approach for integrating computer vision into robotic pipeline navigation. A subsequent study, “Water pipe valve detection by using deep neural networks” (2020, 12 citations), further refined these techniques, demonstrating how deep neural networks can enhance the accuracy and reliability of identifying buried utilities. Through this work, Wu has provided a scalable, data-driven solution that supports decision-making for pipe replacement and rehabilitation, directly improving the safety and efficiency of water supply systems. Her research is a compelling example of how AI and robotics can solve real-world infrastructure problems, making her a notable figure in smart city and civil engineering innovation.