Abstract:With the increasing number of urban rail transit, the cracks, water leakage, spalling and other diseases caused by the deterioration of its own performance and the surrounding environment have gradually appeared. The manual inspection limited by factors such as light and sight distance, and the detection quality and efficiency have become more and more unable to meet the operational requirements. In order to improve the detection efficiency and quality, a set of efficient detection equipment for urban rail transit tunnels is developed and applied in the urban rail. The equipment can realize the rapid collection of tunnel apparent disease images under high-speed rail driving, which greatly improves the efficiency of field detection. The intelligent identification of cracks, water leakage, spalling and other diseases are realized through the supporting image processing and artificial intelligence algorithms. Compared with manual inspection, the data results are richer, more accurate and more efficient, which has the good application prospects.
