Mask R-CNN-based underground drainage pipeline disease pixel level detection method

Abstract

The invention is applicable to the technical field of intelligent segmentation of pipeline diseases, and relates to a Mask RCNN-based underground drainage pipeline disease pixel level detection method, which comprises the following steps of: making an underground drainage pipeline instance segmentation data set by utilizing an acquired drainage pipeline disease video; a loss function and an ROI pooling layer in a Mask R-CNN deep learning architecture being optimized, and a ResNet101 network being used as a pipeline disease feature extraction network, so that the detection precision of an instance segmentation algorithm is improved; initializing network model parameters by using a transfer learning technology, performing a hyper-parameter tuning test on the network model, and starting network model training; evaluating the performance of the training network model, and analyzing the intersection-combination ratio of the network model prediction disease area and the real disease area; and judging whether the network model can achieve a pixel-level segmentation effect or not. According to the method, the Mask R-CNN instance segmentation framework, the ResNet101 residual neural network and the drainage pipeline disease big data are combined, so that rapid, accurate and automatic identification and positioning of the underground drainage pipeline disease are realized.

Description

technical field [0001] The invention belongs to the technical field of pipeline disease intelligent segmentation, and in particular relates to a pixel-level detection method for underground drainage pipeline diseases based on Mask R-CNN. Background technique [0002] The underground drainage pipe network is an important infrastructure of a modern city, the material basis and an indispensable lifeline for the city's survival and development, and shoulders the heavy responsibility of urban sewage and rainwater discharge. With the development of my country's economy, the scale of urban construction is getting bigger and bigger, the underground pipelines are getting longer and longer, and the remaining service life of the pipelines is getting shorter and shorter. The safety hazards caused by the aging and disrepair of underground drainage pipelines are also highlighted. Insufficient discharge capacity of drainage pipes due to diseases such as scale and sediment deposition is one ...

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a detection method based on Mask R-CNN at the pixel level of underground drainage pipeline diseases, so as to solve the problem of low detection accuracy and efficiency of underground drainage pipeline diseases in the prior art

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