Papers
2
Total Citations
41
H-Index
2
About
Yujian Mei is a researcher advancing the field of nondestructive evaluation through the integration of deep learning and robotic automation. Her primary research areas include ultrasonic imaging, robot-assisted inspection, and deep learning-based image reconstruction for complex and curved industrial components. Mei’s most notable contribution is the development of the Visual Geometry Group-UNet (VGG-UNet), a deep learning framework designed to reconstruct high-quality ultrasonic images of curved parts. This work, published in 2021 and garnering 38 citations, directly addresses the longstanding challenge of detecting small defects in curved geometries using classical monostatic pulse-echo methods. By combining a robot-assisted ultrasonic testing system with a track-scan imaging approach, she significantly improved detection coverage and image contrast. In related work from 2020, Mei explored a robot-assisted track-scan method using multiple incident angles to enhance imaging on complexly structured parts, further demonstrating her commitment to overcoming the limitations of conventional B-scan techniques. Her research bridges robotics, signal processing, and artificial intelligence, offering practical solutions for modern manufacturing inspection.
Research Focus
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Top Papers
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