Hajer Fradi
Papers
2
Total Citations
23
H-Index
2
About
Hajer Fradi is a leading researcher in computer vision and thermal imaging, with a focus on enhancing object detection in challenging environments. Her work primarily addresses the limitations of infrared cameras, such as low contrast and blurred details, which hinder pedestrian detection in surveillance, robotics, and night vision applications. Fradi’s most cited paper, “Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection” (2021, 16 citations), introduces a novel GAN-based approach to improve thermal image quality, significantly boosting detection accuracy. She further advances this field with her 2022 study, “Feature distribution alignments for object detection in the thermal domain” (7 citations), which tackles domain adaptation by aligning feature distributions, enabling robust detection across varying thermal conditions. Fradi’s contributions are pivotal for real-world systems requiring reliable night vision and all-weather performance. Her innovative use of deep learning to bridge the gap between thermal and visible domains marks her as a key figure in intelligent surveillance, with growing citation impact reflecting the practical importance of her research.
Research Focus
Key Achievements
Top Papers
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