Water level estimation in sewage pipes using texture-based methods and machine learning algorithms.

Abstract

Water companies use closed-circuit television (CCTV) to inspect the condition of sewage pipes. The reports generated by surveyors help companies to plan for the maintenance and rehabilitation of sewage pipes. A surveyor needs to record the water level at the start of every survey and any point of significant change in level. Recording the water level provides insight into the cross-section area being surveyed, highlighting any underlying issues with the pipe. An abrupt change in water level can indicate a poor gradient of pipe, a build-up of debris, or even hidden structural damage. However, manually recorded water levels are often unreliable due to factors like surveyor experience, the camera angle, light conditions, and pipe shape. In this paper, we have discussed and compared six methods for the automated estimation of water levels in sewage pipes. Using the segmentation masks extracted with DeepLabv3 as inputs into an Extra Trees regressor achieved the most accurate results. To perform an objective comparison of the techniques, mean absolute error (MAE), root mean square error (RMSE), and max error were used as evaluation metrics.