Tunnel maintenance requires a complicated and constraining visual inspection. In order to automate this task, we propose to evaluate and compare three statistical learning algorithms, a random forest and two convolutional networks, dedicated to the detection of defects (e.g. cracks) on tunnel linings. Each model is trained on datasets of our own, consisting of images of concrete walls and masonry walls. We show that these learning-based approaches are competitive with the state of the art on this application domain.