Fereshteh Aghaee
Papers
1
Total Citations
3
H-Index
1
About
Fereshteh Aghaee is a researcher whose work sits at the intersection of computer vision and embedded systems, with a particular focus on multispectral pedestrian detection. Her most cited paper, "MDSSD-MobV2: An embedded deconvolutional multispectral pedestrian detection based on SSD-MobileNetV2" (2023), introduces a novel approach that combines deconvolutional techniques with the lightweight SSD-MobileNetV2 architecture. This work addresses the critical challenge of deploying accurate pedestrian detection on resource-constrained devices, such as autonomous vehicles and surveillance systems, by fusing RGB and thermal (multispectral) data. The proposed method enhances detection performance while maintaining computational efficiency, a key trade-off in real-world applications. Though early in her career, with 3 citations to date, Aghaee’s contribution is notable for its practical orientation—bridging state-of-the-art deep learning with edge deployment. Her research holds promise for safer autonomous navigation and intelligent monitoring systems, demonstrating how embedded vision can be both robust and efficient.
Research Focus
Key Achievements
Top Papers
- 1