Lihui Cen

Papers

Total Citations

H-Index

About

Lihui Cen is a researcher at the forefront of intelligent systems, with a primary focus on computer vision and robotic control. His work bridges the gap between perception and action, developing algorithms that enable machines to see and move with greater autonomy. Cen’s most impactful contribution is in real-time underwater object detection, where he proposed an improved YOLOv7 architecture. This work, published in 2025 and already garnering 10 citations, addresses the critical challenges of low visibility and dynamic lighting in subsea environments, offering a robust solution for autonomous underwater vehicles. In robotics, Cen tackled the inefficiencies of deep reinforcement learning for arm path planning. His 2024 paper introduces a Phased Reward Configuration Mechanism (PRCM), which strategically divides the learning process to enhance obstacle avoidance and trajectory efficiency—a notable achievement that reduces randomness in DRL training. By combining cutting-edge neural network design with innovative reinforcement learning strategies, Cen is advancing the capabilities of autonomous systems in complex, real-world settings. His research holds significant promise for applications in marine exploration and industrial automation.