Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network

Abstract

Regular inspection and repair of drainage pipes is an important part of urban construction. Currently, many classification methods have been used for defect diagnosis using images inside pipelines. However, most of these classification models train the classifier with the goal of maximizing accuracy without considering the unequal error classification cost in defect diagnosis. In this study, the authors analyze the characteristics of sewer pipeline defect detection and design an automated detection framework based on the cost-sensitive deep convolutional neural network (CNN). The method makes the CNN network cost sensitive by introducing learning theories at the structural and loss levels of the network. To minimize misclassification costs, the authors propose a new auxiliary loss function Cost-Mean Loss, which allows the model to obtain the original parameters of the network to maximize the accuracy and improve the performance of the model by minimizing total misclassification costs in the learning process. Theoretical analysis shows that the new auxiliary loss function can be applied to the classification task to optimize the expected value of misclassification costs. The inspection images collected from multiple drainage pipes were used to train and test the network. Results show that after the cost-sensitive strategy was added, the defect detection rate decreased from 2.1% to 0.45%. Moreover, the model with Cost-Mean Loss has better performance than the original model.