Zhuliang Chen
Papers
1
Total Citations
3
H-Index
1
About
Zhuliang Chen is at the forefront of applying computer vision and geospatial technologies to infrastructure quality control. His research centers on real-time defect detection and geometric verification in underground construction, particularly pipe jacking projects. Chen’s major contribution lies in developing an integrated monitoring framework that combines binocular-camera systems with YOLOv5-based deep learning for high-precision defect detection, while simultaneously using handheld LiDAR point cloud analysis for continuous geometric verification of pipeline trajectories. His most cited work, "High-Precision Defect Detection and Geometric Verification in Pipe Jacking Projects Using Computer Vision and Point Cloud Data" (2025), has already garnered 3 citations, demonstrating early impact in this niche but critical field. By enabling proactive quality control that simultaneously addresses structural defects and alignment deviations, Chen’s research promises to reduce costly rework and enhance safety in underground infrastructure. His work represents a significant step toward fully automated, real-time construction monitoring, positioning him as an emerging leader in the intersection of computer vision, geospatial analysis, and civil engineering.
Research Focus
Key Achievements
Top Papers
- 1