Logo des Repositoriums
Zur Startseite
  • English
  • Deutsch
Anmelden
  1. Startseite
  2. SuUB
  3. Bibliographie HS Bremen
  4. On the detection and classification of objects in scarce sidescan sonar image dataset with deep learning methods
 

On the detection and classification of objects in scarce sidescan sonar image dataset with deep learning methods

Veröffentlichungsdatum
2023
Autoren
Steiniger, Yannik  
Stoppe, Jannis  
Kraus, Dieter  
Meisen, Tobias  
Zusammenfassung
Applying deep learning detection methods to sonar imagery is a challenging task due to the complexity of the image itself as well as the limited amount of available data. In this work, we analyze one-step and two-step setups for detection multiple different objects in sidescan sonar images. The one-step setup and the first step in the two-step setup uses standard deep learning models, like YOLOv8, to either directly locate and classify the objects or to serve as a snippet extractor. In the second step these extracted snippets are further classified by a convolutional neural network. Furthermore, we investigate a setup in which the detected objects from the one-step approach are filtered by another CNN to reduce false alarms. Finally, we compare the performance of multiple deep learning detectors to a classical two-step approach using template matching combined with a CNN. Our results show that both two-step setups generate less false alarms. Furthermore, all deep learning models outperform the template matching approach.
Schlagwörter
deep learning

; 

Automatic target recognition

; 

Sonar imagery
Institution
Hochschule Bremen  
Fachbereich
Hochschule Bremen - Fakultät 4: Elektrotechnik und Informatik  
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
7th Underwater Acoustics Conference and Exhibition, UACE2023  
Startseite
353
Endseite
362
Sprache
Englisch

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Datenschutzbestimmungen
  • Endnutzervereinbarung
  • Feedback schicken