用模糊规则让污水管缺陷评估既准又可解释。
A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks
- 先用Swim Transformer识图，再用规则推理得分，过程可追溯。
- 比纯图像识别提升17.9%准确率，五类严重度不平衡下表现稳定。
- 适合需要透明决策的市政管道巡检系统，尤其关注可解释性。
传统自动化污水管缺陷评估依赖图像分类，形成难以解释的“黑箱”。本研究提出一种模块化模糊规则神经符号框架，将感知与推理分离。感知模块采用Swin Transformer，直接从图像预测14个多重标签的检测代码（CODE）度量。推理模块使用J48决策树在真实标注的CODE与严重度标签上训练，生成19条固定IF--THEN规则。推理通过模糊逻辑实现：以t-范数激活CODE条件，按规则置信度加权后与对应s-范数合并，生成可解释的类别证据。我们使用3244张图像、5个高度不平衡的严重度类别的数据集，评估了乘积、Łukasiewicz和哈马彻算子组合。真实标签由五名独立大语言模型分析原始检查笔记达成共识生成。结果表明，相比纯图像分类，该方法在准确率、平衡准确率、宏平均F1和马修相关系数上分别提升17.9%、12.2%、23.0%和17.3%。整体框架兼具优异的类别平衡性能与从预测CODE到规则支持及严重度证据的可追溯推理路径。
原文摘要 · Abstract (English)
Standard automated sewer pipe severity assessment relies on direct image classification, creating a "black box" where the link between visual defects and final severity scores remains implicit. This study introduces a modular, fuzzy rule-based neuro-symbolic framework that bridges this gap by decoupling neural perception from symbolic reasoning. The perception module utilizes a Swin Transformer to predict 14 multilabel inspection CODE degrees directly from images. For reasoning, a DT, specifically Weka's J48, algorithm is trained on ground-truth CODEs and severity labels, and its paths are converted into 19 fixed IF--THEN rules. Inference operates via fuzzy logic: t-norm activations from CODE conditions are weighted by rule confidence and combined with corresponding s-norms to produce interpretable class evidence. We assessed Product, Łukasiewicz, and Hamacher operator pairs using a dataset of 3,244 images spanning five highly imbalanced severity classes. Ground-truth labels were robustly generated via consensus from five independent large language models analyzing original inspector notes. Our results show an improvement of accuracy, balanced accuracy, Macro F1 and MCC by 17.9%, 12.2%, 23.0%, and 17.3%, respectively, over image-only based classification. Overall, the framework combines competitive class-balanced performance with traceable reasoning from predicted CODE degrees to rule supports and severity evidence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。