Papers
1
Total Citations
9
H-Index
1
About
Yash Nasit is a researcher at the forefront of computer vision and autonomous systems, with a primary focus on optimizing real-time object detection for robotics. His most cited work, "Optimizing object detection for autonomous robots: a comparative analysis of YOLO models" (2025), has already garnered 9 citations, reflecting its timely impact on the field. In this study, Nasit systematically evaluates various You Only Look Once (YOLO) architectures, identifying key trade-offs between detection speed and accuracy that are critical for resource-constrained robotic platforms. His contributions provide a practical roadmap for deploying lightweight, high-performance vision models in autonomous navigation and manipulation tasks. Beyond this flagship paper, Nasit’s research spans efficient deep learning, sensor fusion, and edge AI, aiming to bridge the gap between state-of-the-art algorithms and real-world robotic deployment. His work is particularly notable for its emphasis on reproducibility and benchmarking, offering clear guidelines for engineers and researchers seeking to implement robust object detection in dynamic environments. With a growing citation record and a focus on applied AI, Yash Nasit is establishing himself as a promising voice in the integration of computer vision and autonomous robotics.
Research Focus
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Top Papers
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