Cracks on the surfaces inside road tunnels are among the most critical problems in the management of such tunnels and, if not properly addressed, can have severe consequences in relation to safety and costs. Nowadays, the main techniques used for analysis of such surfaces make use of either human inspections or complex automated systems, which are, respectively, very time consuming and expensive, and/or difficult to implement. There is therefore great interest in a low-cost data-acquisition platform coupled with Artificial Intelligence-based automated crack detection system. This paper introduces a low-cost technique for road tunnel inspections based on a simple system that does not require complex preliminary work and can also be used in tunnels with a normal traffic flow. In particular, thanks to a second-generation data-acquisition system developed in the 2017–2018 academic year, a series of high-resolution pictures can be obtained and used in a pre-trained deep neural network able to identify the presence of cracks through the classification of the pictures. Thanks to deep learning techniques, it is possible to exploit the power of Inception-v4, a deep convolutional neural network provided by Google, which can be retrained for the specific purpose of crack detection. This kind of network has been trained with a pictures database generated using a second data-acquisition prototype developed during the 2017–2018 academic year.