Nafiz Fahad

Papers

Total Citations

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About

Nafiz Fahad is a researcher specializing in computer vision and deep learning, with a particular focus on efficient object detection for complex, real-world environments. His most-cited work, "Efficient Object Detection with an Optimized YOLOv8x Model" (2025), tackles critical challenges in indoor scene understanding—such as occlusions, variable lighting, and clutter—by systematically evaluating YOLOv8 variants from nano to extra-large. Fahad’s key contribution lies in developing an optimized YOLOv8x architecture that balances accuracy and computational efficiency, making it suitable for deployment in resource-constrained settings. Though early in his career, with this paper already garnering 3 citations, his work demonstrates strong potential for impact in robotics, surveillance, and smart building applications. By addressing practical barriers to real-time detection, Fahad is helping to bridge the gap between state-of-the-art models and deployable systems. His research is especially valuable for students and engineers seeking to adapt cutting-edge object detection frameworks to challenging, non-ideal conditions.