Najoua Ben Amara

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About

Najoua Ben Amara’s research lies at the intersection of computer vision, mobile robotics, and intelligent transportation systems, with a particular focus on real-time object detection and recognition. Her most-cited work, “Performance Benchmarking of YOLO Architectures for Vehicle License Plate Detection from Real-time Videos Captured by a Mobile Robot” (2021), has garnered 6 citations and exemplifies her commitment to deploying deep learning in dynamic, real-world environments. In this study, she systematically evaluated state-of-the-art YOLO architectures for license plate detection, addressing challenges like motion blur, varying lighting, and perspective distortion—critical for autonomous surveillance and traffic monitoring. Her contributions advance the practical integration of lightweight neural networks on resource-constrained robotic platforms, bridging the gap between algorithmic performance and field deployment. Ben Amara’s work is particularly notable for its emphasis on benchmarking and reproducibility, providing a valuable reference for researchers developing vision-based systems for mobile robots. Her research continues to influence the design of efficient, real-time detection pipelines, making her a key voice in the evolving dialogue between computer vision and robotics.