Junyong Xia
Papers
2
Total Citations
20
H-Index
2
About
Junyong Xia is a researcher at the forefront of intelligent robotics and industrial automation, with a focus on computer vision and optimization algorithms. His work bridges the gap between deep learning and practical robotic systems, particularly in high-precision maintenance and manufacturing tasks. Xia’s most cited paper, "Fast Rail Fastener Screw Detection for Vision-Based Fastener Screw Maintenance Robot Using Deep Learning" (2024), has already garnered 12 citations, introducing the lightweight FSS-YOLO model to enable real-time, accurate detection for railway maintenance robots. This innovation addresses a critical need for speed and efficiency in infrastructure upkeep. In another key contribution, "Structural parameters identification for industrial robot using a hybrid algorithm" (2022, 8 citations), Xia proposed a novel hybrid optimization algorithm combining adaptive genetic and simulated annealing methods to enhance robot precision and reduce movement uncertainty. By improving global search capabilities, this work advances the reliability of industrial robots. With a growing citation impact and a focus on deployable, real-world solutions, Junyong Xia is establishing himself as a promising voice in the evolution of intelligent robotic systems for critical infrastructure and manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2