Mayasa M. Abdulrahman
Papers
1
Total Citations
5
H-Index
1
About
Dr. Mayasa M. Abdulrahman is a rising expert in computer vision and autonomous systems, with a focused emphasis on real-time object detection and pedestrian safety. Her most-cited work, "Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades" (2024, 5 citations), addresses a critical bottleneck in modern AI-driven technologies—balancing detection accuracy with computational efficiency. By integrating enhanced YOLO architectures with complexity-aware cascades, Dr. Abdulrahman’s research directly improves the reliability of autonomous vehicles, surveillance systems, and robotics, where split-second decisions can prevent accidents. Her contributions are particularly notable for tackling the challenge of detecting pedestrians in dynamic, cluttered environments, a key hurdle for widespread deployment of safe autonomous systems. Though early in her career, her work has already garnered attention for its practical, deployable solutions. Dr. Abdulrahman’s research stands at the intersection of deep learning and real-world safety, promising to shape the next generation of intelligent, responsive machines.
Research Focus
Key Achievements
Top Papers
- 1