Jialiang Yuan

Shanxi Agricultural University, Shanghai Electric Cable Research Institute

Papers

Total Citations

H-Index

About

Jialiang Yuan is a researcher specializing in computer vision and intelligent robotics, with a focus on automated detection and inspection in complex environments. His most impactful work, "Weed Detection with Improved YOLOv7" (2023, 13 citations), addresses a critical challenge in precision agriculture: accurately identifying weeds amidst complex field backgrounds. Yuan enhanced the YOLOv7 model through online data augmentation, optimizing feature extraction, fusion, and point judgment to improve detection robustness. This contribution offers a scalable solution for reducing herbicide use and boosting crop yields. Additionally, his 2021 paper on "Automatic Inspection Method of Cable Tunnel in Complex Environment Based on Quadruped Robot" (3 citations) pioneers the use of legged robotics for infrastructure monitoring. By integrating theoretical research with field demands, Yuan developed a method that ensures reliable inspection distance and quality in challenging tunnel conditions. His work bridges the gap between advanced AI models and practical robotic applications, demonstrating significant potential for automation in both agriculture and industrial maintenance.