Yosuke Ida
Papers
1
Total Citations
8
H-Index
1
About
Yosuke Ida is a robotics and computer vision researcher whose work centers on real-time object detection and its deployment in dynamic, unstructured environments. His most-cited study, "Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments" (2024, 8 citations), offers a rigorous comparative analysis of these leading YOLO frameworks. Critically, Ida’s findings challenge the prevailing assumption that newer model versions are universally superior, revealing nuanced trade-offs between precision, adaptability, and computational efficiency in robotic contexts. This work provides essential guidance for engineers selecting detection architectures for latency-sensitive applications. Beyond this study, Ida’s broader contributions address the integration of deep learning with robotic perception, emphasizing robustness under real-world constraints such as motion blur and variable lighting. His research is particularly valuable for autonomous systems, where reliable object detection directly impacts safety and task success. By systematically evaluating model performance beyond benchmark scores, Ida helps bridge the gap between theoretical advances in computer vision and practical robotic deployment. His work continues to influence the design of efficient, adaptive perception pipelines for next-generation autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1