Abstract: Municipal drainage systems are critical to the safety of urban road traffic， so it is important to assess their condition. In developed countries， closed-circuit television （CCTV） is the main detection tool for sewer assessment and maintenance， but it brings new challenges for its data processing. This paper proposes a drainage network defect detection method based on recurrent convolutional neural network （RCNN）. The RCNN uses a residual network （ResNet） as feature extraction module to extract visual features of drainage network image sequences， and a bidirectional LSTM is used to learn to identify temporal features to accomplish drainage network defect classification task. The method recognizes image sequences as a whole， and the training set， validation set and test set contain a total of 8800 image sequences， and 211200 images. The data set are trained and tested by the RCNN model， and the highest accuracy rate of the test set is 90.3%. Six sets of control experiments are carried out with four different fusion methods introduced to the proposed method， the SVM-based method and the method based on single frames， as well as three fusion methods based on visual attention mechanism are introduced into the proposed method and control tests are carried out. The experimental results show that the highest accuracy （90.3%） of the fusion experiments is achieved by RCNN taking the average value， and the feasibility analysis of engineering applications is realized， and the recall rate of RCNN reaches 0.977， which confirms the feasibility of the proposed method in engineering applications.

Key words:  , municipal drainage pipe network； convolutional neural network； recurrent neural network