Papers
2
Total Citations
7
H-Index
2
About
Ruili Liu’s research focuses on computer vision and image processing for infrastructure inspection, particularly in challenging underground environments. Her major contributions lie in developing specialized dehazing algorithms to enhance pipeline inspection imagery, which is often degraded by water fog, darkness, and haze. Liu pioneered a pipeline image haze removal system using dark channel prior on a cloud processing platform (2020, 5 citations), significantly improving detection performance for pipeline robots monitoring underground drainage facilities. She further advanced this work with a dehazing algorithm based on atmospheric scattering models and multi-scale Retinex strategy (2019, 2 citations), addressing the persistent problem of blurred detection videos caused by pipeline water mist. Her research directly tackles critical real-world challenges in urban infrastructure maintenance, where clear imaging is essential for fault detection and safety. By combining atmospheric physics models with adaptive enhancement techniques, Liu’s work enables more reliable automated inspection of drainage systems, reducing the need for manual visual assessment. Her contributions represent important steps toward practical, cloud-based solutions for maintaining the security of underground pipeline facilities through improved image quality in extreme environmental conditions.
Research Focus
Key Achievements
Top Papers
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