Hamid Noori
Papers
1
Total Citations
3
H-Index
1
About
Hamid Noori is a researcher whose work sits at the intersection of computer vision, embedded systems, and deep learning, with a particular focus on efficient, real-time object detection. His most cited contribution, "MDSSD-MobV2: An embedded deconvolutional multispectral pedestrian detection based on SSD-MobileNetV2" (2023), exemplifies his drive to make advanced AI models deployable on resource-constrained devices. By integrating deconvolutional techniques with the lightweight SSD-MobileNetV2 architecture and multispectral imaging, Noori addresses the critical challenge of pedestrian detection under varying lighting and weather conditions—a key requirement for autonomous driving and smart surveillance. While his citation count is still growing, this work has already garnered attention for its practical, hardware-aware approach. Noori’s research is notable for bridging the gap between high-accuracy detection algorithms and the real-world limitations of embedded platforms, such as low power and memory. His contributions are particularly relevant for students and engineers seeking to understand how to optimize deep neural networks for edge deployment, making him a valuable voice in the evolving field of efficient computer vision.
Research Focus
Key Achievements
Top Papers
- 1