Edge-Guided Multi-Scale Fusion and Importance-Aware Learning for Real-Time Semantic Segmentation in Waterborne Navigation

Effective multi-scale feature representation and focused attention on critical objects are essential for accurate perception of waterborne navigation scenes. To address the insufficient exploitation of multi-scale information in existing methods that leads to imprecise segmentation, this study propo...

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Autors principals:	Li-Jia Chen, Jiamin Zou, Yao Huang, Yang Zhou, Guozhu Hao, Yi Zhang
Format:	Artigo
Idioma:	Inglês
Publicat:	Gdynia Maritime University 2025-06-01
Col·lecció:	TransNav
Matèries:	collision avoidance computer vision artificial intelligence (ai) navigation systems autonomous ships semantic segmentation obstacle detection water traffic management
Accés en línia:	https://www.transnav.eu/files/EdgeGuided_MultiScale_Fusion_and_ImportanceAware_Learning_for_RealTime_Semantic_Segmentation_in_Waterborne_Navigation,1540.pdf
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