Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
| one publication added to basket [354379] |
On the automatic text detection and recognition algorithms for maritime images
Nita, C.; Vandewal, M. (2021). On the automatic text detection and recognition algorithms for maritime images. Proc. SPIE Int. Soc. Opt. Eng. 11870: 118700B. https://dx.doi.org/10.1117/12.2599422
In: Proceedings of SPIE, the International Society for Optical Engineering. SPIE: Bellingham, WA. ISSN 0277-786X; e-ISSN 1996-756X, meer
|  |
| Beschikbaar in | Auteurs |
|
Documenttype: Congresbijdrage
|
| Trefwoord |
|
| Author keywords |
Ship intelligence; deep neural network; automatic vessel identification; text detection and recognition; maritime domain awareness |
| Abstract |
In view of the increase in illicit maritime activities like piracy, sea robbery, trafficking of narcotics, immigration and illegal fishing, an enhance of accuracy in surveillance is essential in order to ensure safer, cleaner and more secure maritime and inland waterways. Recently, the field of deep learning technology has received a considerable attention for integration into the security systems and devices. Convolutional Neural Networks (CNN) are commonly used in application of object detection, segmentation and classification. In addition, they are used for text detection and recognition, mainly applied to automatic license plate recognition for the highway monitoring, rarely to the maritime situational awareness. In the current study, we propose to analyse the practical feasibility of applying an automatic text detection and recognition algorithm on ship images. We consider a two-stage procedure that localizes the text region and then decodes the prediction into a machine-readable format. In the first stage the text region in the scene is localized with computer-vision based algorithms and EAST model, whereas in the second stage the predicted region is decoded by the Tesseract Optical Character Recognition (OCR) engine. Our results demonstrate that the integration of such a feature into a vessel information system will most likely improve the overall situational awareness. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
