Ling Ding
Papers
5
Total Citations
217
H-Index
4
About
Ling Ding is a prominent researcher at the intersection of robotics, computer vision, and civil engineering infrastructure maintenance, with a particular focus on automated pavement crack detection, tracking, and repair. His work addresses one of the most persistent challenges in infrastructure management: developing intelligent, autonomous systems capable of identifying and sealing road cracks with precision and efficiency. Ding's most influential contribution, a 2022 survey on automated guided vehicles and autonomous mobile robots in civil engineering (151 citations), established a foundational reference for the field. Building on this, he has pioneered vision-guided robotic systems for pixel-level pavement crack sealing, advancing adaptive fuzzy control and transformer-based tracking algorithms that enable robots to navigate unstructured road environments reliably. His 2025 work on lightweight distillation networks for edge devices demonstrates a commitment to making these solutions practically deployable in real-world settings. With a cumulative citation count exceeding 200 across his key publications, Ding's research has gained meaningful traction within both the robotics and civil engineering communities. His collaborations with international researchers, including connections to institutions such as Cambridge, further underscore his growing influence in intelligent infrastructure automation.
Research Focus
Key Achievements
Top Papers
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- 4Vision-guided robot for automated pixel-level pavement crack sealing17 citations · 2024
- 5