Abdelhalim Azam
Papers
1
Total Citations
11
H-Index
1
About
Abdelhalim Azam is a researcher at the forefront of intelligent transportation systems and deep learning for autonomous mobility. His work focuses on solving critical bottlenecks in computer vision for self-driving vehicles, particularly the challenge of generating large-scale, labelled training datasets without prohibitive human effort. His most cited paper, "A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility" (2023, 11 citations), introduces a novel generative network that synthesizes realistic road scenes, dramatically reducing the manual annotation burden. This contribution is pivotal for advancing urban mobility solutions, enabling more efficient training of perception models. Azam’s research bridges generative adversarial networks and lightweight architectures, making deep learning pipelines more accessible and scalable. His work is widely recognized for its practical impact on autonomous driving and smart city infrastructure, positioning him as an emerging voice in applied AI for transportation.
Research Focus
Key Achievements
Top Papers
- 1