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Yin, Wenjie
Publications (1 of 1) Show all publications
Shi, J., Yin, W., Du, Y. & Folkesson, J. (2019). Automated Underwater Pipeline Damage Detection using Neural Nets. In: : . Paper presented at ICRA 2019 Workshop on Underwater Robotics Perception.
Open this publication in new window or tab >>Automated Underwater Pipeline Damage Detection using Neural Nets
2019 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Pipeline inspection is a very human intensive taskand automation could improve efficiencies significantly. We propose a system that could allow an autonomous underwater vehicle (AUV), to detect pipeline damage in a stream of images.Our classifiers were based on transfer learning from pre-trained convolutional neural networks (CNN). This allows us to achieve good results despite relatively few training examples of damage. We test the approach using data from an actual pipeline inspection.

National Category
Computer Systems Robotics
Research subject
Computer Science
urn:nbn:se:kth:diva-256311 (URN)
ICRA 2019 Workshop on Underwater Robotics Perception
Swedish Foundation for Strategic Research , IRC15-0046

QC 20190827

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-27Bibliographically approved

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