Licheng Zong

Papers

Total Citations

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About

Dr. Licheng Zong is a researcher focused on advancing computer vision and robotics, with key contributions in object detection for autonomous systems. His most cited work, "Model Adaption Object Detection System for Robot" (2020, 14 citations), addresses a critical challenge in robotics: enabling reliable object detection despite changing viewpoints and limited training data during robot movement. By proposing a novel vision system that adapts to dynamic environments, Dr. Zong’s research bridges the gap between static model training and real-world robotic deployment. This work has been recognized for its practical significance in autonomous navigation and manipulation, providing a foundation for more adaptive and resilient robotic perception. Dr. Zong’s contributions are particularly valuable for students and researchers working at the intersection of computer vision and robotics, offering insights into model adaptation techniques that overcome data scarcity and viewpoint variability. His research continues to influence the development of intelligent systems capable of robust performance in unstructured environments.