Muhammad Amir Abdulrrozaq
Papers
1
Total Citations
2
H-Index
1
About
Muhammad Amir Abdulrrozaq is a researcher at the forefront of real-time computer vision and robotics, with a particular focus on deploying deep learning models in resource-constrained, dynamic environments. His work centers on optimizing object detection algorithms for autonomous systems, especially humanoid soccer robots, where split-second decisions are critical. In his highly cited 2024 paper, "Optimizing YOLOv8 for Real-Time Performance in Humanoid Soccer Robots with OpenVINO," Abdulrrozaq tackles the challenge of balancing speed and accuracy in vision systems. He demonstrates how to leverage Intel’s OpenVINO toolkit to accelerate YOLOv8, a state-of-the-art object detection model, achieving significant performance gains without sacrificing detection quality. This contribution is vital for enabling robots to perceive and react to their surroundings in real time, advancing the field of robotic soccer and broader autonomous navigation. With 2 citations already, his work is gaining traction among researchers seeking efficient AI deployment on edge devices. Abdulrrozaq’s research bridges the gap between cutting-edge deep learning and practical, real-world robotics, making him a key figure in the evolution of intelligent, responsive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1