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About

Jianqi Zhang is a pioneering researcher at the intersection of robotics, computer vision, and civil infrastructure engineering, with a particular focus on automated pavement inspection and repair. His work addresses one of transportation engineering's most persistent challenges: the efficient detection, diagnosis, and treatment of road distress across its full lifecycle. Zhang's most celebrated contribution — garnering 151 citations — examines how automated guided vehicles and autonomous mobile robots can revolutionize recognition and tracking tasks in civil engineering, establishing him as a leading voice in construction automation. Building on this foundation, he has developed sophisticated vision-guided robotic systems capable of pixel-level pavement crack segmentation, tracking, and sealing, integrating cutting-edge deep learning architectures including transformers and lightweight neural networks optimized for edge deployment. A hallmark of Zhang's research is its end-to-end practicality: he bridges perception and action by coupling advanced crack segmentation models with intelligent controllers such as neural-PID and adaptive fuzzy systems, enabling autonomous robots to operate reliably in unstructured real-world environments. His work on knowledge distillation and lightweight model design further demonstrates a commitment to deployable, resource-efficient solutions. With nearly 330 cumulative citations and multiple publications in 2024–2025 alone, Zhang's contributions are rapidly shaping the future of intelligent infrastructure maintenance.