Ahmed Lateef Khalaf
Papers
1
Total Citations
5
H-Index
1
About
Dr. Ahmed Lateef Khalaf is a leading researcher in computer vision and real-time object detection, with a primary focus on enhancing pedestrian and object detection for safety-critical applications such as autonomous vehicles, surveillance systems, and robotics. His most-cited work, "Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades" (2024), introduces a novel framework that significantly improves detection accuracy while maintaining computational efficiency. By integrating complexity-aware cascades with the YOLO architecture, Dr. Khalaf addresses the dual challenge of speed and precision in dynamic environments. This contribution has garnered 5 citations in a short time, reflecting its growing influence in the field. His research is notable for bridging the gap between theoretical advances and practical deployment, offering scalable solutions for real-world systems. Dr. Khalaf’s work continues to shape the development of safer, more responsive autonomous technologies, making him a key figure in the evolution of intelligent detection systems.
Research Focus
Key Achievements
Top Papers
- 1