Jingbo Liu
Papers
2
Total Citations
88
H-Index
2
About
Jingbo Liu is a researcher advancing the field of intelligent construction monitoring through computer vision and deep learning. Their primary research areas include object detection algorithms, machine vision for quality control, and real-time structural monitoring in civil engineering. Liu’s most notable contribution is the development of SDS-YOLO, an enhanced vibratory position detection algorithm built upon the YOLOv11 framework, which significantly improves the accuracy and speed of identifying vibration positions during concrete placement—a critical task for ensuring structural integrity. This work has garnered 74 citations since its 2024 publication, reflecting its immediate impact on automated construction inspection. Additionally, Liu introduced a temporal fusion strategy for machine vision, enabling robust monitoring of concrete vibration quality by analyzing sequential image data, a method that has already attracted 14 citations. By bridging cutting-edge AI with practical construction challenges, Liu’s research offers scalable solutions for reducing human error and enhancing quality assurance in large-scale infrastructure projects. Their work is essential reading for engineers and computer scientists interested in the intersection of deep learning and real-time industrial monitoring.
Research Focus
Key Achievements
Top Papers
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