Automatic Defect Detection in Sewer Network Using Deep Learning Based Object Detector

Bach Ha, Birgit Schalter, Laura White, Joachim Köhler

Abstract: Maintaining sewer systems in large cities is important, but also time and effort consuming, because visual inspections are currently done manually. To reduce the amount of aforementioned manual work, defects within sewer pipes should be located and classified automatically. In the past, multiple works have attempted solving this problem using classical image processing, machine learning, or a combination of those. However, each provided solution only focus on detecting a limited set of defect/structure types, such as fissure, root, and/or connection. Furthermore, due to the use of hand-crafted features and small training datasets, generalization is also problematic. In order to overcome these deficits, a sizable dataset with 14.7 km of various sewer pipes were annotated by sewer maintenance experts in the scope of this work. On top of that, an object detector (EfficientDet-D0) was trained for automatic defect detection. From the result of several expermients, peculiar natures of defe