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  • 1.
    Shi, Jiajun
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Yin, Wenjie
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Du, Yipai
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Automated Underwater Pipeline Damage Detection using Neural Nets2019Conference paper (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.

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