Ghazlane Yasmine
Papers
1
Total Citations
15
H-Index
1
About
Yasmine Ghazlane is a researcher at the forefront of computer vision and drone detection technology. Her work centers on developing real-time, lightweight models for identifying and classifying unmanned aerial vehicles, a critical need for security and airspace management. Her most-cited paper, "Real-time lightweight drone detection model: Fine-grained Identification of four types of drones based on an improved Yolov7 model" (2024), has already garnered 15 citations, reflecting its immediate impact. In this work, Ghazlane enhances the YOLOv7 architecture to achieve high-accuracy, fine-grained classification of four distinct drone types while maintaining computational efficiency—a breakthrough for edge deployment. Her contributions address the challenge of balancing detection speed with precision, enabling practical surveillance systems that can operate on resource-constrained devices. By advancing lightweight neural networks for drone identification, Ghazlane is shaping the future of autonomous security and aerial monitoring.
Research Focus
Key Achievements
Top Papers
- 1