Bhupendra Fataniya
Papers
1
Total Citations
2
H-Index
1
About
Bhupendra Fataniya is a researcher at the forefront of real-time computer vision and embedded artificial intelligence, with a primary focus on object detection and edge computing. His most notable contribution is the development of SD-YOLOv5, a specialized deep learning model for real-time staircase detection, which he successfully implemented on the resource-constrained NVIDIA Jetson Nano board. This work, published in 2025 and already garnering citations, addresses a critical challenge in assistive robotics and autonomous navigation for visually impaired individuals, demonstrating how lightweight neural architectures can be deployed on low-power hardware without sacrificing accuracy. Fataniya’s research bridges the gap between state-of-the-art detection algorithms and practical, deployable systems, making him a key contributor to the growing field of edge AI. His work not only advances computer vision applications but also underscores the importance of accessibility in technology, with potential impacts on smart mobility aids and autonomous indoor navigation. By optimizing YOLOv5 for real-time performance on embedded devices, he has provided a scalable framework for future research in domain-specific object detection.
Research Focus
Key Achievements
Top Papers
- 1