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Objectness scoring and detection proposals in forward-looking sonar images with convolutional neural networks Valdenegro-Toro, M. (2016). Objectness scoring and detection proposals in forward-looking sonar images with convolutional neural networks, in: Schwenker, F. et al. Artificial neural networks in pattern recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28–30, 2016, Proceedings. Lecture Notes in Computer Science, 9896: pp. 209-219. https://dx.doi.org/10.1007/978-3-319-46182-3_18 In: Schwenker, F. et al. (2016). Artificial neural networks in pattern recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28–30, 2016, Proceedings. Lecture Notes in Computer Science, 9896. Springer: Cham. ISBN 978-3-319-46181-6; e-ISBN 978-3-319-46182-3. XI, 335 pp. https://dx.doi.org/10.1007/978-3-319-46182-3 In: Lecture Notes in Computer Science. Springer-Verlag: Heidelberg; Berlin. ISSN 0302-9743; e-ISSN 1611-3349

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Beschikbaar in	Auteur
VLIZ: Non-open access 371059 [ aanvragen ]
Documenttype: Congresbijdrage

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Author keywords

Object detection; Detection proposals; Sonar image processing; Forward-looking sonar

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Auteur		Top
Valdenegro-Toro, M.

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Abstract

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional Neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94 % recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

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