Fan Chen

Xi'an Jiaotong University, Chinese Academy of Sciences

Papers

Total Citations

H-Index

About

Fan Chen has carved a distinctive niche at the intersection of machine vision and robotic perception, focusing on how machines can interpret and interact with complex, cluttered environments. Their foundational work, the 2021 "Literature Review of Machine Vision in Application Field" (7 citations), provides a comprehensive taxonomy of visual inspection and robot vision systems, establishing a critical framework for understanding the architecture and advantages of modern machine vision. Chen’s most technically significant contribution, "Object Recognition and 3D Pose Estimation Using Improved VGG16 Deep Neural Network in Cluttered Scenes" (2018, 2 citations), directly tackles one of robotics’ most persistent challenges: accurate object detection and pose estimation in visually noisy environments. By enhancing the VGG16 deep neural network, Chen developed a method that robustly recognizes objects and estimates their three-dimensional orientation even when partially occluded or surrounded by clutter—a breakthrough for autonomous manipulation and industrial automation. This work demonstrates a keen ability to adapt deep learning architectures for real-world, unstructured settings. Though early in their career, Chen’s focused contributions to machine vision and 3D pose estimation signal a promising trajectory in advancing robotic-environment interaction.