V S Divya Sundar
Papers
1
Total Citations
29
H-Index
1
About
V S Divya Sundar is a rising researcher in computer vision and deep learning, whose work focuses on making object detection systems resilient to real-world environmental challenges. Her most-cited paper, "Enhancing Robust Object Detection in Weather-Impacted Environments using Deep Learning Techniques" (2024, 29 citations), introduces R-YOLO (Robust You Only Look Once), a novel framework that adapts the YOLO architecture to mitigate noise and improve visibility under adverse weather conditions. This contribution is critical for autonomous systems and surveillance, where reliability in rain, fog, or snow is paramount. By integrating adaptive preprocessing and feature enhancement, Sundar’s work directly addresses a key bottleneck in deploying AI in uncontrolled outdoor settings. Her research bridges the gap between theoretical robustness and practical deployment, earning early recognition for its potential in safety-critical applications. With a growing citation footprint, Sundar is establishing herself as a thoughtful innovator in robust deep learning, and her work is already informing next-generation object detection pipelines.
Research Focus
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Top Papers
- 1