Gufran Ahmad
Papers
1
Total Citations
3
H-Index
1
About
Gufran Ahmad is a rising researcher in computer vision and deep learning, with a focused interest in real-time object detection for autonomous systems and intelligent surveillance. His most-cited work, "Real-Time Vehicle Detection using YOLOv8 and Data Augmentation Approach" (2023), demonstrates a practical application of the cutting-edge YOLOv8 architecture, enhanced through strategic data augmentation to improve detection accuracy in dynamic environments. This study addresses critical challenges in autonomous vehicle perception and traffic monitoring, showcasing Ahmad's ability to bridge state-of-the-art algorithms with real-world deployment needs. With 3 citations already, this paper signals growing recognition of his contributions to efficient, high-performance vision systems. Ahmad's research emphasizes the optimization of lightweight models for edge deployment, a vital direction for scalable AI. As an emerging voice in the field, his work not only advances YOLO-based detection but also provides a reproducible framework for researchers tackling similar object detection tasks. His trajectory suggests a deepening impact on applied computer vision, particularly where speed and accuracy must coexist.
Research Focus
Key Achievements
Top Papers
- 1Real-Time Vehicle Detection using YOLOv8 and Data Augmentation Approach3 citations · 2023