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This paper presents the experimental results of an automated sensor system for the inspection of tubular structures. The method is applied to the autonomous inspection of sewers overcoming the drawbacks of standard CCTV-based inspection systems. The transducer consists of a low-cost laser-based profiler attached to a standard CCTV camera. Image analysis techniques and artificial neural networks are used to automatically locate and classify the defects in the pipe using the intensity distribution in the acquired camera images. A wide range of tests using data from different types of pipes in realistic conditions have been conducted and are presented here. It is shown that the proposed inspection approach is particularly well suited to complement existing CCTV inspection systems, providing automated and reliable detection of pipe defects in the millimeter range.