Kenji Fujimoto

Papers

Total Citations

H-Index

About

Kenji Fujimoto is a researcher at the forefront of applying computer vision and deep learning to environmental robotics. His primary research focus lies in developing efficient, real-time object detection algorithms for autonomous systems, with a particular emphasis on tackling the pressing issue of urban and marine pollution. Fujimoto’s most notable contribution is his work on the "Trash Detection Algorithm Suitable for Mobile Robots Using Improved YOLO" (2023), which has already garnered 10 citations. This study addresses the critical challenge of illegal dumping of aluminum and plastic, which severely impacts ecosystems and increases environmental pollution. By enhancing the YOLO (You Only Look Once) architecture for mobile robotic platforms, Fujimoto has created a lightweight yet highly accurate detection system that can be deployed on resource-constrained robots. This innovation promises to significantly reduce the effort, time, and cost associated with traditional volunteer-based trash cleanup operations. His work represents a vital step toward scalable, autonomous environmental remediation, bridging the gap between state-of-the-art AI and practical, real-world conservation efforts.