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.

维护大城市的下水道系统十分重要，但也十分耗时耗力，因为目前的视觉检查都是手动完成的。为了减少手动工作量，应当自动定位和分类下水道管道中的缺陷。本研究通过大规模数据集的注释和深度学习的物体检测方法，解决了识别缺陷以及训练时的困难，最终依靠自动检测器能够在测试集中检测到 83% 的缺陷。这项工作将深度学习物体检测应用到重要但相对较为无声的工程领域，并给出了一些在注释特殊 “对象”（如缺陷）时的实用指导。