Xudong Bai

Papers

Total Citations

H-Index

About

Xudong Bai’s research focuses on the intersection of computer vision and robotics, with particular emphasis on practical object detection and region-of-interest (ROI) extraction for autonomous systems. His most-cited work, “A Practical ROI and Object Detection Method for Vision Robot” (2020), addresses a fundamental challenge in visual robotics: how to efficiently and accurately identify objects while filtering out irrelevant visual information to improve processing speed. By proposing a targeted ROI-based detection framework, Bai’s method enables robots to focus computational resources on critical regions, thereby enhancing real-time performance in dynamic environments. Though his citation count remains modest, this contribution is significant for its practical orientation—bridging theoretical detection algorithms with real-world robotic applications. Bai’s work is particularly valuable for researchers developing cost-effective or resource-constrained robotic systems, where efficient visual processing is paramount. His approach demonstrates a clear understanding of the trade-offs between accuracy and computational efficiency, making it a useful reference for those working on vision-guided robotics in manufacturing, service, or exploration contexts.