Abstrakt
This article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.
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- Wersja publikacji
- Accepted albo Published Version
- Licencja
- otwiera się w nowej karcie
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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Polish Maritime Research
nr 27,
strony 170 - 178,
ISSN: 1233-2585 - Język:
- angielski
- Rok wydania:
- 2020
- Opis bibliograficzny:
- Bobkowska K., Bodus-Olkowska Izabela I.: Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification// Polish Maritime Research -Vol. 27,iss. 4(108) (2020), s.170-178
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.2478/pomr-2020-0077
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 100 razy
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Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
- K. Bobkowska,
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