Khushi Bhardwaj
Papers
1
Total Citations
31
H-Index
1
About
Khushi Bhardwaj is a rising researcher in computer vision and deep learning, whose work centers on advancing real-time object detection for critical applications in robotics, autonomous vehicles, and surveillance systems. Her most cited paper, "Deep Learning Approach for Multi-Object Detection Using Yolo Algorithm" (2023, 31 citations), makes a significant contribution by systematically exploring the You Only Look Once (YOLO) framework—a method prized for balancing high accuracy with real-time performance. Bhardwaj’s analysis not only demystifies YOLO’s architecture but also demonstrates its practical deployment in complex multi-object scenarios, providing a clear roadmap for researchers and engineers seeking efficient detection solutions. This work has quickly gained traction, reflecting its relevance in a field where speed and precision are paramount. Bhardwaj’s research underscores her ability to bridge theoretical deep learning advances with tangible, real-world systems, positioning her as a promising voice in the ongoing evolution of intelligent visual perception. Her growing citation count signals that her insights are already shaping how next-generation autonomous and surveillance technologies perceive and interact with their environments.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Approach for Multi-Object Detection Using Yolo Algorithm31 citations · 2023