Yigong Zhang

Nanjing University of Science and Technology

Papers

Total Citations

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About

Yigong Zhang is a robotics and autonomous systems researcher whose work focuses on perception and navigation for intelligent vehicles and industrial automation. His primary research areas include curb detection for autonomous driving, illumination-invariant road detection, and robust object positioning for visual robotics. Zhang’s most impactful contribution is his 2018 paper on curb detection, which has garnered 58 citations and addresses a critical challenge in autonomous driving—accurately detecting curbs despite occlusions, shadows, and the target’s small size. He further advanced urban road detection with an illumination-invariant nonparametric model (14 citations), which uses a monocular camera and single-line LIDAR to remove shadows and improve performance under varying lighting conditions. More recently, Zhang has tackled data-scarce environments in industrial settings, developing a robust object positioning method for visual robotics in automatic assembly lines (2022). His work bridges the gap between autonomous navigation in outdoor environments and precision automation in manufacturing, demonstrating versatility in applying computer vision and sensor fusion to real-world problems. Zhang’s research is particularly valuable for students and engineers working on perception systems that must operate reliably under challenging conditions, from urban streets to factory floors.