Abstract—Worldwide, sewer networks are designed to transport wastewater to a centralized treatment plant to be treated and returned to the environment. This is a critical process for preventing waterborne illnesses, providing safe drinking water and enhancing general sanitation in society. To keep a perfectly operational sewer network several inspections are manually performed by a Closed-Circuit Television system to report the obstruction level which may trigger a cleaning operative. In this work, we design a methodology to train a Convolutional Neural Network (CNN) for identifying the level of obstruction in pipes. We gathered a database of videos to generate useful frames to fed into the model. Our resulting classifier obtains deployment ready performances. To validate the consistency of the approach and its industrial applicability, we integrate the Layer-wise Relevance Propagation (LPR) algorithm, which endows a further understanding of the neural network behavior. The proposed system provides higher speed, accuracy, and consistency in the sewer process examination.

Index Terms—artificial intelligence, computer vision, pattern recognition, video recognition, deep learning, convolutional neural networks, explainability, sewers

Cite: Mario A. Gutiérrez-Mondragón, Dario Garcia-Gasulla, Sergio Alvarez-Napagao, Jaume Brossa-Ordoñez, and Rafael Gimenez-Esteban, "Obstruction Level Detection of Sewers Videos Using Convolutional Neural Networks," International Journal of Structural and Civil Engineering Research, Vol. 10, No. 4, pp. 135-143, November 2021. doi: 10.18178/ijscer.10.4.135-143

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