电力信息与通信技术2026，Vol.24Issue(5)：32-39,8.DOI:10.16543/j.2095-641x.electric.power.ict.2026.05.04
基于广义指代表达分割的缺陷检测和行为识别方法研究
Research on Defect Detection and Action Recognition Methods Based on Generalized Referring Expression Segmentation
摘要
Abstract
In the field of power vision,existing defect detection methods based on deep learning usually take a single image modality as input,resulting in poor accuracy for complex defect types.Additionally,most of the existing defect detection methods based on image-text fusion target one or several specific types of defects and cannot be effectively promoted.This paper proposes a defect detection problem modeling method based on generalized referring expression segmentation.It forms a complete defect detection dataset construction process by building an ontology library,a professional knowledge base,and the mapping relationship between the two.Furthermore,the feasibility of this method is verified on the annotated dataset through an image-text fusion model based on relationship modeling.Experiments show that the image-text fusion model trained under the generalized referring expression segmentation framework achieves an average precision and recall rate of 80%for the recognition of 4 typical targets in the ladder operation scenario,which is significantly better than the visual model trained under the target detection framework.Moreover,the method proposed in this paper also has significant advantages in aspects such as fine-grained detection and interpretability.关键词
广义指代表达分割/电力/缺陷检测/行为识别/图文融合Key words
generalized referring expression segmentation/power/defect detection/action recognition/image-text fusion分类
信息技术与安全科学引用本文复制引用
胡方舟,宋睿,张屹,张桉恺,常政威,刘超..基于广义指代表达分割的缺陷检测和行为识别方法研究[J].电力信息与通信技术,2026,24(5):32-39,8.基金项目
国家电网有限公司总部科技项目"基于图文模型的多目标可解释现场作业违章行为识别关键技术研究及应用"(5700-202426249A-1-1-ZN). （5700-202426249A-1-1-ZN）
