Manaf Zghaibeh
Papers
1
Total Citations
14
H-Index
1
About
Manaf Zghaibeh is a researcher whose work lies at the intersection of autonomous systems and computer vision, with a particular focus on enhancing the safety and reliability of self-driving vehicles. His most cited paper, "Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles" (2022, 14 citations), addresses a critical and growing challenge: the detection of small, often overlooked obstacles on highways that can lead to severe accidents. By leveraging semantic segmentation, Zghaibeh’s work provides a robust framework for identifying these hazards, thereby improving the decision-making capabilities of autonomous vehicles in real-world, dynamic environments. This contribution is especially significant given the increasing deployment of autonomous and robotic systems across industrial applications. His research not only advances the technical frontier of obstacle detection but also underscores a commitment to practical, life-saving innovations in transportation safety. Zghaibeh’s work is a valuable resource for students and researchers exploring the nuances of autonomous navigation, computer vision, and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1