J. Roshini Roy
Papers
1
Total Citations
6
H-Index
1
About
J. Roshini Roy is a researcher at the forefront of applying deep learning to autonomous underwater systems. Her work centers on the detection and classification of submerged objects, addressing the profound challenges of exploring the world’s oceans—vital sources of ecological balance and sustenance. Roy’s most cited paper, “Under Water Objects Detection and Classification using Deep Learning Technique” (2024, 6 citations), introduces a pioneering approach that integrates convolutional neural networks with autonomous robotic platforms. This method enables real-time identification of marine objects, significantly enhancing the efficiency and accuracy of underwater surveys. By bridging computer vision and robotics, Roy’s contributions hold transformative potential for marine biology, environmental monitoring, and underwater infrastructure inspection. Her research tackles the critical bottleneck of limited visibility and complex underwater environments, offering a scalable solution for autonomous exploration. Though early in her career, Roy’s work has already garnered attention for its practical impact, laying the groundwork for safer, more intelligent oceanic discovery. Her dedication to merging cutting-edge AI with real-world aquatic challenges marks her as an emerging leader in marine robotics.
Research Focus
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Top Papers
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