AI-enabled Underground Water Pipe non -destructive Inspection

Georgios-Fotios Angelis, Dimitrios Chorozoglou, Stavros Papadopoulos, Anastasios Drosou, Dimitrios Giakoumis, Dimitrios Tzovaras

Abstract: A study conducted by the World Bank indicated that the global annual economic losses from the water leakage are estimated at US$ 14.6 billion. For this reason, locating and repairing water leaks as well as the maintenance of water pipelines is extremely important for the optimization and rationalization of water resources. The basic technique for inspecting water delivery infrastructure is the water audit but this technique does not provide any information about the location of the water leakage. This paper focuses on this gap, aiming to provide information not only for the location of the water leakage but also for the level of water pipe material degradation due to its corrosion before the leakage presents. Here, the identification of the extent and severity of the evolving defect of water pipes is performed through deep learning models using simulated and real Ground Penetrating Radar (GPR) data. Synthetic GPR images are generated, with underground water pipes that either present leakage or no in various steps of their corrosion, using gprMax software. Especially, this addresses as a solution YOLOv5 algorithm for the automatic detection of water pipes and leaks in the underground space and a conditional Generative Adversarial Network (cGAN) for the investigation of water pipe material degradation. The results reveal that the YOLOv5 algorithm distinguishes the regions of pipes in GPR data and classified correctly the pipes which present leakage or no, and they are better than the corresponding results of other literature baseline methods. In addition, as shown through extensive simulations on generated GPR data the proposed cGAN produces high quality results that contribute to revelation of the extent and severity of the evolving defect of pipeline due to its corrosion.