  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 defects
in the context of object detection, which greatly effect annotation and
training process, are found and discussed. At the end, the final detector was
able to detect 83% of defects in the test set; out of the missing 17%, only
0.77% are very severe defects. This work provides an example of applying deep
learning-based object detection into an important but quiet engineering field.
It also gives some practical pointers on how to annotate peculiar "object",
such as defects.
