Abstract: To promote the deep integration of domain knowledge of the underground pavement with the object detection algorithm, alleviate the feature distortion caused by the feature complexity and similarity between different disease samples and improve the automatic disease detection effect, proposed a detecting airport pavement underground disease algorithm integrated association reasoning. First, a feature extraction method combined residual networks and multi-scale feature pyramid modules was used to extract disease feature information. Second, by mining the correlation matrix of airport pavement underground disease, a module for underground disease association reasoning was designed based on graph inference. The feature vectors generated by the regional proposal network were used as input features, and self-learning weight matrices were used as disease correlation weights to achieve integration of the association reasoning module. The experimental results demonstrated that the airport pavement underground disease detection algorithm integrating association reasoning effectively utilized the correlation relationships between underground diseases, eliminated mutual interference between defect samples, and achieved optimal detection performance with an average accuracy of 87.38%.

摘要： 为促进道面地下领域知识和目标检测算法的深度融合，缓解不同病害样本间的特征复杂性和相似性导致的特征畸变问题，提升病害的自动化检测效果，提出了融合关联关系推理的机场道面地下病害检测算法。首先，本算法结合残差网络和多尺度特征金字塔模块（Feature Pyramid Network，FPN）提取目标特征信息;其次，通过挖掘机场道面地下病害关联关系矩阵，结合图推理设计地下病害关联关系推理模块，以区域生成网络(Region Proposal Network，RPN)生成的特征向量作为输入特征，利用自我学习的变换矩阵设定图的传播权重，实现特征信息传播并构建有效的关联关系推理模块。实验证明，融合关联关系推理的机场道面地下病害检测算法可以有效地利用地下病害之间的关联关系，消除病害之间的相互干扰并且检测效果达到最优，平均准确率达到了87.38%。