Mohammed Al-Refai
Papers
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Total Citations
1
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About
Mohammed Al-Refai is a computer vision researcher whose work focuses on advancing object detection in challenging visual environments, particularly low-light and dark conditions. His most-cited paper, "Performance Evaluation of YOLOv7 for Object Detection in Dark Environments" (2025), provides a critical benchmark for deploying the YOLO algorithm in security, surveillance, and robotics applications where conventional detection systems often fail. By systematically evaluating YOLOv7’s robustness under degraded visibility, Al-Refai identifies key modifications that enhance detection accuracy without sacrificing speed, offering practical insights for real-world deployment. This work has already garnered attention within the computer vision community, accumulating citations that underscore its relevance to both academic research and industry applications. Al-Refai’s contributions are particularly valuable for autonomous systems operating at night, smart surveillance networks, and search-and-rescue robotics. His research bridges the gap between state-of-the-art deep learning models and the practical constraints of uncontrolled environments, making him a notable emerging voice in the field of vision-based perception under adverse conditions.
Research Focus
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Top Papers
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