Regular inspections of underground infrastructure are mandatory to maintain structural integrity and traffic safety. Standard procedures rely on visual inspection, complemented by testing for internal defects of the tunnel lining with the hammering method. This testing method uses manual excitation with a hammer and interpretation of the perceived sound by an engineer or technician. Result quality is based on the experience of the executing personal and thus highly subjective. The presented project aims at the automation of this procedure to enable a fast and objective inspection covering the whole tunnel lining. To achieve this, a new laser-based measurement system for detecting near surface defects in large concrete structures is developed. Surface vibrations are induced with plasma ignition and measured with a Doppler Vibrometer. The system has motorized mirror adjustment and can thus obtain fast-paced measurements on a grid. We present the experimental setup, the measurements performed on a concrete test block with artificial defects and the algorithms for automated defect detection. From the measured eigenfrequencies, we derive the size and location of defects. Computer vision techniques are adapted to make the system resilient against variable environmental conditions, like noise and temperature, or changes in construction material and age. These sophisticated signal processing methods are applied to extract relevant information from the measurements and are combined with topological data to provide service personnel with detailed maps for maintenance work.