Febri Alwan Putra
Papers
1
Total Citations
5
H-Index
1
About
Febri Alwan Putra is a robotics and computer vision researcher whose work focuses on enabling autonomous perception for humanoid robots, particularly in dynamic, real-time environments like soccer fields. His most cited paper, "XNOR-YOLO: The High Precision of The Ball and Goal Detecting on The Barelang-FC Robot Soccer" (2020), addresses a critical challenge in robot vision: balancing detection speed with accuracy. By adapting the YOLO object detection framework with XNOR-based binarized neural networks, Putra developed a lightweight yet high-precision system that allows humanoid robots to reliably identify balls and goals during competitive play. This contribution directly supports the Barelang-FC robot soccer team, demonstrating practical impact in the RoboCup domain. With 5 citations, his work has influenced subsequent research in efficient neural network deployment on resource-constrained robotic platforms. Putra’s research sits at the intersection of embedded AI, real-time object detection, and autonomous robotics, offering scalable solutions for robots that must perceive and react instantly. His achievements highlight a commitment to bridging theoretical advances in deep learning with tangible, field-tested robotic applications.
Research Focus
Key Achievements
Top Papers
- 1