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Object Detection in Sonar Images

: Karimanzira, Divas; Renkewitz, Helge; Shea, David; Albiez, Jan

Fulltext ()

Electronics. Online journal 9 (2020), No.7, Art. 1180, 16 pp.
ISSN: 2079-9292
Journal Article, Electronic Publication
Fraunhofer IOSB ()
deep learning; underwater sonar images

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.