Shehnila Zardari

NED University of Engineering and Technology

Papers

Total Citations

H-Index

About

Shehnila Zardari is a leading researcher at the intersection of computer vision, robotics, and artificial intelligence, with a specialized focus on domain adaptation. Her work addresses a critical challenge in modern AI: enabling machine learning models to perform reliably across different environments without costly retraining. Her highly cited 2023 review, *"An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision"* (20 citations), provides a comprehensive technical roadmap of the field, systematically analyzing both the opportunities and persistent challenges in making vision systems adaptable. Building on this foundation, her 2024 study, *"Advancing Industrial Object Detection Through Domain Adaptation: A Solution for Industry 5.0"* (10 citations), demonstrates the practical application of these techniques to 3D datasets for industrial object detection, directly supporting the human-centric, resilient manufacturing goals of Industry 5.0. By bridging theoretical frameworks with real-world industrial solutions, Zardari is shaping how autonomous systems perceive and interact with dynamic environments, making her work essential reading for researchers and engineers developing robust, generalizable vision systems for the next generation of robotics and smart manufacturing.