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  4. A study on modern deep learning detection algorithms for automatic target recognition in sidescan sonar images
 
Verlagslink DOI
10.1121/2.0001470

A study on modern deep learning detection algorithms for automatic target recognition in sidescan sonar images

Veröffentlichungsdatum
2021
Autoren
Steiniger, Yannik  
Groen, Johannes  
Stoppe, Jannis Ulrich  
Kraus, Dieter  
Meisen, Tobias  
Zusammenfassung
State-of-the art deep learning models have shown remarkable performance on computer vision tasks like object classification or detection. These networks are typically trained on large-scale datasets of natural RGB images. However, sidescan sonar images are gray-scaled images representing acoustic intensities. The fundamental differences between camera and sonar as well as the images itself makes it necessary to investigate the transfer of results achieved on RGB images to the sonar imagery domain. Therefore, we compare the deep learning detection algorithm YOLOv2 with its updated version YOLOv3, both adopted for object detection in sidescan sonar images. In addition to this, a small convolutional neural network (CNN) is trained from scratch and used for detection. The experiments answer two questions: First, whether, as for general computer vision problems, transfer learning of large deep learning models is preferable over training of custom networks when dealing with limited sonar data. Secondly, whether improvements in the YOLO architecture, developed based on RGB images, lead to significant improvements on sonar data as well. Our results show that YOLOv3 indeed performs better than YOLOv2. Furthermore, YOLOv3 achieves a true positive rate of up to 98.2% and outperforms the small CNN.
Verlag
American Instut. of Physics
Institution
Hochschule Bremen  
Fachbereich
Hochschule Bremen - Fakultät 4: Elektrotechnik und Informatik  
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Proceedings of Meetings on Acoustics  
Serie(s)
6th Underwater Acoustics Conference and Exhibition  
Heft
44
Startseite
070010
Sprache
Englisch

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