T. Hemanth Babu

Papers

Total Citations

H-Index

About

T. Hemanth Babu is a researcher at the forefront of autonomous driving and computer vision, with a focus on developing cost-effective, vision-based frameworks for real-world navigation. His most-cited work, "A Low-Cost Vision-Based Framework for Autonomous Driving Using YOLO, MiDaS, and Stereo Vision" (2025), integrates state-of-the-art deep learning models—YOLO for object detection, MiDaS for depth estimation, and stereo vision for spatial awareness—to create a scalable, affordable alternative to expensive sensor suites like LiDAR. This framework demonstrates how combining monocular and stereo cues can achieve robust perception for self-driving vehicles, addressing critical challenges in cost and accessibility. With 2 citations already in its early publication, the paper signals growing interest in his approach to democratizing autonomous driving technology. Babu’s contributions lie at the intersection of efficient deep learning and practical robotics, offering a blueprint for low-cost systems that maintain high accuracy. His work is particularly notable for its potential to accelerate the deployment of autonomous vehicles in developing regions, where budget constraints often limit innovation. For students and researchers, Babu’s research exemplifies how clever integration of existing tools can yield transformative results in a field dominated by high-end hardware.