中南民族大学学报（自然科学版）2026，Vol.45Issue(2)：221-230,10.DOI:10.20056/j.cnki.ZNMDZK.20250848
基于CT-ST半监督模型的城市地下管道缺陷语义分割研究
Semantic segmentation of urban underground pipeline defects based on CT-ST semi-supervised model
摘要
Abstract
Defect segmentation of urban underground pipes using machine vision technology is an industrialized intelligent development trend.Since conventional supervised methods require a large number of annotations for defect segmentation task,an improved CT-ST semi-supervised semantic segmentation model based on the ST semi-supervised model is proposed,which is firstly applied to the field of defect segmentation of urban underground pipelines.The model is based on the self-training method of semi-supervised semantic segmentation domain,combined with the idea of Co-teaching algorithm,distinguishes different quality pseudo-labels,and utilizes a one-time pseudo-label screening strategy instead of the traditional set-threshold iterative method,to reduce the impact of erroneous feature training due to low-quality labels;for the problems of complex background of underground pipelines,multiple defect categories,multiple scales,and multiple noises,we introduce a NAM attention mechanism into each residual block,to give each important defects a more accurate and more accurate labeling.NAM attention mechanism is introduced in each residual block to increase the weight of each important feature and weaken the proportion of unimportant features.The experiments verify the effectiveness of CT-ST semi-supervised segmentation model,and the mIoU is improved on different proportions of labeled sample sets,in which the mIoU of 1/2 proportion of labeled dataset is 67.36%,which is increased by 2.33%compared with the original model.Compared with many mainstream pseudo-labeling and consistency regularization methods,CT-ST has better performance in terms of accuracy.关键词
半监督学习/ST模型/Co-teaching算法/注意力机制/伪标签/地下管道缺陷/缺陷分割Key words
semi-supervised learning/ST model/Co-teaching algorithm/attention mechanism/pseudo labels/defects in urban underground pipelines/defect segmentation分类
信息技术与安全科学引用本文复制引用
潘禹欣,李波,田淙文,姚为..基于CT-ST半监督模型的城市地下管道缺陷语义分割研究[J].中南民族大学学报（自然科学版）,2026,45(2):221-230,10.基金项目
国家自然科学基金资助项目(61976226) （61976226）
