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  4. Object Detection in Sonar Images
 
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2020
Journal Article
Title

Object Detection in Sonar Images

Abstract
The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.
Author(s)
Karimanzira, Divas
IOSB-AST
Renkewitz, Helge
IOSB-AST
Shea, David
Kraken Robotics, 189 Glencoe Drive, Mount Pearl, NL A1N 4P6, Canada
Albiez, Jan
Kraken Robotik, 28197 Bremen, Germany
Journal
Electronics. Online journal  
Open Access
DOI
10.3390/electronics9071180
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • deep learning

  • underwater sonar images

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