Masahito Fukuda

Papers

Total Citations

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About

Masahito Fukuda is a robotics researcher whose work centers on computer vision, autonomous navigation, and sustainable robot software platforms. He is best known for his contributions to garbage detection using deep learning, specifically applying YOLOv3 in the Nakanoshima Challenge—a real-world robot demonstration experiment. This work, cited 7 times, addresses critical challenges in training data creation for object detectors, highlighting the labor-intensive nature of human annotations and proposing more efficient solutions. Fukuda also made significant strides in robotics competition infrastructure with his proposal of a highly sustainable robot software platform (4 citations). This research focuses on enabling teams in competitions like the Tsukuba Challenge to leverage open-source software effectively, ensuring long-term viability and reproducibility of robotic systems. His work bridges the gap between cutting-edge deep learning applications and practical, sustainable robotics deployment. By tackling both algorithmic efficiency and software sustainability, Fukuda has contributed to making autonomous systems more accessible and robust for real-world challenges, particularly in outdoor environments.