Balaji Ganesh Rajagopal

Papers

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About

Balaji Ganesh Rajagopal is a researcher at the forefront of intelligent transportation systems and efficient computer vision. His work centers on solving critical challenges in autonomous driving and video analytics, particularly the labor-intensive process of creating large labeled datasets for deep learning. His most influential contribution, "A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility" (2023, 11 citations), introduces a novel generator network that synthesizes realistic urban road scenes, dramatically reducing the need for manual data annotation. This innovation accelerates the development of perception systems for self-driving vehicles. Rajagopal also advances real-time object detection with "A novel and optimized YOLO model for H.265 encoded video frames" (2025, 4 citations), addressing the computational demands of modern video compression standards. His research directly impacts the scalability and efficiency of AI in edge computing and autonomous navigation. By blending generative adversarial networks with lightweight architectures, Rajagopal offers practical solutions that bridge the gap between academic research and real-world deployment, making him a notable contributor to the future of urban mobility and intelligent video processing.