计算机工程2025，Vol.51Issue(2)：397-406,10.DOI:10.19678/j.issn.1000-3428.0069060
融合关联关系推理的机场道面地下病害检测算法
Airport Pavement Underground Disease Detection Algorithm Integrating Association Reasoning
摘要
Abstract
To promote the deep integration of domain knowledge on underground pavement with object detection algorithms,alleviate feature distortion caused by feature complexity and similarity among different disease samples,and enhance automatic disease detection,an airport pavement underground disease detection algorithm integrating association reasoning is proposed.First,the proposed method combines a residual network and a multi-scale Feature Pyramid Network(FPN)to extract target feature information.Second,a module for underground disease association reasoning is designed based on graph inference,leveraging the correlation matrix of airport pavement underground diseases.The feature vectors generated by the Regional Proposal Network(RPN)are used as input features,and self-learning transformation matrices are employed to set the propagation weight of the graph.This enables feature information propagation and constructs an effective association reasoning module.Experimental results demonstrate that the airport pavement underground disease detection algorithm integrating association reasoning effectively utilizes the correlation relationships between underground diseases,eliminates mutual interference among defect samples,and achieves optimal detection performance,with an average accuracy of 87.38％.关键词
机场道面/地下病害/领域知识/目标检测/关联关系推理Key words
airport pavement/underground disease/domain knowledge/object detection/association reasoning分类
信息技术与安全科学引用本文复制引用
李海丰,刘森森,王怀超,李南莎,张艺凡..融合关联关系推理的机场道面地下病害检测算法[J].计算机工程,2025,51(2):397-406,10.基金项目
国家重点研发计划(2021YFB1600502) （2021YFB1600502）
国家自然科学基金(62373365) （62373365）
成都市"揭榜挂帅"科技项目(2021-JB00-00025-GX) （2021-JB00-00025-GX）
中央高校基本科研业务费专项(3122022PY13) （3122022PY13）
天津市教委科研计划项目(2021KJ036). （2021KJ036）
