Pranav Adarsh

Delhi Technological University, Amrita Vishwa Vidyapeetham

Papers

Total Citations

535

H-Index

About

Pranav Adarsh is a researcher at the forefront of efficient deep learning and autonomous systems, with a primary focus on real-time object detection and low-power artificial intelligence for robotics. His most influential contribution, the 2020 paper "YOLO v3-Tiny: Object Detection and Recognition using one stage improved model," has garnered 532 citations, reflecting its significant impact on the field. In this work, Adarsh advanced the widely-used YOLO architecture, optimizing it for speed and accuracy in resource-constrained environments—a critical step for applications like pedestrian detection and autonomous navigation. He further explored the intersection of hardware and AI in his 2019 study on a low-power AI processor for autonomous mobile robots, addressing the growing demand for efficient decision-making in devices from room cleaners to delivery drones. By tackling the dual challenges of algorithmic efficiency and energy consumption, Adarsh has contributed to making deep learning more accessible for real-world, edge-computing scenarios. His work bridges the gap between theoretical advances and practical deployment, offering valuable insights for students and researchers aiming to build faster, smarter, and more sustainable autonomous systems.