Shao‐Yu Yang

National Central University, National Taitung University

Papers

Total Citations

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About

Shao‐Yu Yang is a leading researcher at the intersection of computer vision, edge artificial intelligence, and autonomous robotics, with a particular focus on unmanned aerial vehicles (UAVs) and robotic systems for critical applications. His most influential work, "Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks" (2023, 12 citations), introduces a pioneering system that integrates a pruned YOLOv4 architecture with the Robot Operating System (ROS), enabling efficient, real-time target tracking from UAVs. This work directly addresses the computational challenges of deploying deep learning on resource-constrained aerial platforms. Yang further advanced the field with his paper "An Edge AI based Robot System for Search and Rescue Applications" (2021, 4 citations), where he proposed a novel multi-robot system combining drones and multi-legged robots. By integrating Tiny-YOLO into the drone design and utilizing FPGAs for on-device processing, he demonstrated a practical, low-latency solution for locating survivors in disaster scenarios. Yang’s contributions are pivotal for pushing the boundaries of autonomous systems, proving that sophisticated AI can operate effectively at the edge, and his work serves as a cornerstone for future developments in real-time robotic perception and emergency response technologies.