In view of the problems of low automation and dependence on the technical level of professionals in the current CCTV detection defects of underground pipeline networks, an integrated method of image processing and deep learning technology is used to build an intelligent method to assist the detection personnel to quickly and accurately identify the type of pipeline defects. Firstly, ten kinds of typical defect images are collected and processed to generate a sample set ; on this basis, using deep convolutional neural networks AlexNet and ResNet50 as the basic framework, using pre-trained AlexNet and ResNet50 networks to learn pipeline defect features, through Sensitivity analysis optimizes the classification network parameters. Then, the accuracy of the pipeline defect intelligent classification model is verified through the test set, and the effectiveness of the model is verified with specific engineering examples. The results show that the accuracy rates of two networks achieve 92.00% and 96.50% on the test set, and the accuracy of the actual engineering cases reached 85.41% and 87.94%, and the classification effect of ResNet50 was better, with good engineering adaptability. Image processing and deep learning technology can improve the automation and accuracy of the classification of drainage pipeline defects, and it is worth further promotion.