Papers
5
Total Citations
62
H-Index
3
About
Ana Madureira is a leading researcher in underwater robotics, specializing in the integration of deep learning for autonomous underwater vehicles (AUVs). Her work focuses on enhancing real-time environmental perception and navigation in challenging underwater environments. Madureira's major contributions include pioneering sonar-based deep learning techniques for object detection and wall localization, as demonstrated in her highly cited 2025 overview paper (33 citations) and her 2022 work on real-time automatic wall detection (17 citations). She has advanced the field by developing novel models like YOLOX-ViT for side-scan sonar object detection and exploring knowledge distillation to create efficient, smaller models without sacrificing performance. Her research also addresses the practical implementation of deep learning frameworks into AUVs, as seen in her work on the LSTS Toolchain. With a total of over 60 citations across her top papers, Madureira's impact is evident in her ability to bridge theoretical advances with real-world underwater applications. Her earlier work on hybrid meta-heuristics for dynamic scheduling showcases her versatility, but it is her recent deep learning innovations that are shaping the future of autonomous underwater exploration and monitoring.
Research Focus
Key Achievements
Top Papers
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- 3Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection6 citations · 2024
- 4
- 5Hybrid Meta-Heuristics Based System for Dynamic Scheduling3 citations · 2009