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.