Xu Yang
Papers
2
Total Citations
45
H-Index
2
About
Xu Yang is an emerging researcher whose work sits at the intersection of computer vision, robotics, and intelligent infrastructure inspection. Specializing in automated road crack detection and repair, Yang has made notable contributions to the development of lightweight deep learning architectures optimized for edge deployment, as well as adaptive control systems for unmanned mobile robots operating in challenging, unstructured environments. Yang's most-cited work, "Crack Segmentation-Guided Measurement with Lightweight Distillation Network on Edge Device" (2025, 23 citations), demonstrates a practical approach to real-time crack analysis by compressing neural network knowledge for resource-constrained hardware — a critical advancement for field deployment. Complementing this, their 2023 paper on cross-entropy-based adaptive fuzzy control (22 citations) tackles the longstanding challenge of robust visual tracking of road cracks under unpredictable real-world conditions, directly enabling more precise and automated crack sealing operations. Together, these contributions reflect Yang's commitment to bridging the gap between theoretical machine learning and deployable robotic systems for civil infrastructure maintenance. With growing citation impact across both works, Yang is establishing a meaningful research footprint in intelligent transportation systems and autonomous inspection robotics.
Research Focus
Key Achievements
Top Papers
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