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 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.

• Record URL:
  • https://doi.org/10.3390/s22186879
• Record URL:
  • https://www.mdpi.com/1424-8220/22/18/6879
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2022 by the authors. Licensee MDPI, Basel, Switzerland
• Authors:
  • Teixeira, Eduardo
  • Araujo, Beatriz
  • Costa, Victor
  • Mafra, Samuel
  • Figueiredo, Felipe
• Publication Date: 2022

Language

• English

Media Info

• Media Type: Digital/other
• Pagination: 6879
• Serial:
  • Sensors
  • Volume: 22
  • Issue Number: 18
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Detection and identification systems; Neural networks; Proximity detectors; Ships; Surveillance
• Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01859615
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 28 2022 11:40AM