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Literature Review on Ship Localization, Classification, and Detection Methods Based on Optical Sensors and Neural Networks

Object detection is a common application within the computer vision area. Its tasks include the classic challenges of object localization and classification. As a consequence, object detection is a challenging task. Furthermore, this technique is crucial for maritime applications since situational a...

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Detalles Bibliográficos
Autores principales: Teixeira, Eduardo, Araujo, Beatriz, Costa, Victor, Mafra, Samuel, Figueiredo, Felipe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501387/
https://www.ncbi.nlm.nih.gov/pubmed/36146228
http://dx.doi.org/10.3390/s22186879
Descripción
Sumario:Object detection is a common application within the computer vision area. Its tasks include the classic challenges of object localization and classification. As a consequence, object detection is a challenging task. Furthermore, this technique is crucial for maritime applications since situational awareness can bring various benefits to surveillance systems. The literature presents various models to improve automatic target recognition and tracking capabilities that can be applied to and leverage maritime surveillance systems. Therefore, this paper reviews the available models focused on localization, classification, and detection. Moreover, it analyzes several works that apply the discussed models to the maritime surveillance scenario. Finally, it highlights the main opportunities and challenges, encouraging new research in this area.