Papers
4
Total Citations
197
H-Index
4
About
Mingzhu Wang is a leading researcher at the forefront of applying digital technologies to urban infrastructure management, with a particular focus on the construction, maintenance, and inspection of underground systems. Her work bridges civil engineering and computer vision, addressing critical challenges in urban sustainability. Wang’s most impactful contribution is her 2022 paper on the construction and maintenance of urban underground infrastructure with digital technologies, which has garnered 108 citations, establishing a comprehensive framework for integrating digital tools into underground projects. She has also pioneered advancements in automated sewer pipe defect detection, developing a synthetic image generation and augmentation framework (67 citations) that overcomes data scarcity in training deep learning models. Her earlier work on semantic segmentation using deep dilated convolutional neural networks (13 citations) laid the groundwork for pixel-level defect classification from CCTV footage, enabling precise severity assessment. Wang’s research extends to foundational image processing techniques, as demonstrated by her 2016 study on edge detection using wavelet transform and mathematical morphology (9 citations). Through these contributions, she has significantly advanced the automation and intelligence of underground infrastructure inspection, directly impacting urban resilience and maintenance efficiency.
Research Focus
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