Mingming Yu

Shanghai University of Engineering Science

Papers

Total Citations

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About

Mingming Yu is a leading researcher in computer vision and deep learning, with a particular focus on advancing automated construction and infrastructure inspection technologies. His work centers on developing lightweight, efficient models that can be deployed on resource-constrained mobile platforms, bridging the gap between high-accuracy AI and real-world robotic applications. Yu’s most notable contribution is YOLO-FAS, a pioneering lightweight deep learning model for detecting rebar intersection locations and tying status in complex construction environments. This work, published in 2025 and already garnering 4 citations, addresses a critical bottleneck in construction automation: the challenge of running sophisticated recognition algorithms on small mobile robots with limited computational power. By significantly reducing model complexity without sacrificing detection accuracy, Yu’s research enables practical, on-site deployment of AI for quality control and safety monitoring. His approach offers a scalable solution for automating tedious manual inspection tasks, promising to enhance both efficiency and worker safety in the construction industry. Yu’s innovative fusion of computer vision and robotics continues to shape the future of intelligent infrastructure management.