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Wyświetlanie 1-2 z 2
Tytuł:
Potential and use of the googlenet ann for the purposes of inland water ships classification
Autorzy:
Bobkowska, Katarzyna
Bodus-Olkowska, Izabela
Powiązania:
https://bibliotekanauki.pl/articles/1573774.pdf
Data publikacji:
2020
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ship classification
image classification
geoinformatics
artificial intelligence
artificial neural network
Opis:
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.
Źródło:
Polish Maritime Research; 2020, 4; 170-178
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
Autorzy:
Bobkowska, Katarzyna
Bodus-Olkowska, Izabela
Powiązania:
https://bibliotekanauki.pl/articles/2033603.pdf
Data publikacji:
2021
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
image classification
inland vessels
ANN
pre-trained neural network
GoogLeNet
AlexNet
SqeeezeNet
Opis:
Artificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained networks already available. The aim of the SHREC projects (automatic ship recognition and identification) is to classify and identify the vessels based on images obtained from closed-circuit television (CCTV) cameras. For this purpose, a dataset of vessel images was collected during 2018, 2019, and 2020 video measurement campaigns. The authors of this article used three pre-trained neural networks, GoogLeNet, AlexNet, and SqeezeNet, to examine the classification possibility and assess its quality. About 8000 vessel images were used, which were categorized into seven categories: barge, special-purpose service ships, motor yachts with a motorboat, passenger ships, sailing yachts, kayaks, and others. A comparison of the results using neural networks to classify floating inland units is presented.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2021, 67 (139); 91--97
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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