Rohan Vaghela
Papers
1
Total Citations
9
H-Index
1
About
Rohan Vaghela is a researcher at the forefront of autonomous systems and computer vision, with a specialized focus on optimizing real-time object detection for robotic platforms. His most impactful work, "Optimizing object detection for autonomous robots: a comparative analysis of YOLO models" (2025), has already garnered 9 citations, reflecting its immediate relevance in the rapidly evolving field of embodied AI. Vaghela’s major contribution lies in systematically benchmarking and fine-tuning state-of-the-art YOLO architectures—such as YOLOv8 and YOLOv9—for deployment on resource-constrained robotic systems, balancing speed, accuracy, and computational efficiency. By identifying optimal trade-offs between model complexity and real-world performance, his research provides a practical roadmap for engineers integrating vision-based navigation into drones, service robots, and autonomous vehicles. This work not only advances the theoretical understanding of lightweight detection networks but also offers reproducible benchmarks that accelerate applied robotics research. Vaghela’s findings are particularly valuable for students and practitioners seeking to bridge the gap between cutting-edge deep learning and reliable robotic perception in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1