Survey on deep learning based computer vision for sonar imagery
Veröffentlichungsdatum
2022-09
Zusammenfassung
Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.
Schlagwörter
Deep learning
;
Sonar imagery
;
Computer Vision
;
Automatic target recognition
;
Status quo review
Verlag
Elsevier {BV}
Institution
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Band
114
Startseite
Article number 105157
Zweitveröffentlichung
Nein
Sprache
Englisch