Graph Convolutional Network Detection Model for Pipeline Defects Based on Improved Label Graph

A drainage pipe image contains many defects, such as deformation and leakage. Considering that existing convolutional neural networks (CNNs) ignore the label relationships and make it difficult to accurately detect multilabel pipeline images, graph convolutional networks (GCNs) were introduced to model the relationships between different defect labels and improved label graph GCN (ILG-GCN) model was proposed. First, the ILG-GCN model introduced the GCN module based on the original CNN model. GCN used label graphs to force classifiers with symbiosis to be close to each other and obtain classifiers that maintain semantic topology, thereby improving the probability of predicting symbiotic labels. Second, the label graph used by the GCN module to update node information was improved. The improved label graph calculated the adaptive label symbiosis probability for each defect based on the symbiosis strength of the main related labels and assigned different weights to the main related labels according to their symbiosis strength. The experimental results show that the mean average precision value of the proposed model is 95.6%, suggesting that the model can accurately detect multiple pipeline defects simultaneously.