发表机构
Can Tho University; Istituto di Scienza e Tecnologie dell’Informazione, CNR - ISTI（芹苴大学; 意大利国家研究委员会信息科学与技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于模糊规则的神经符号框架，将神经感知与符号推理解耦，结合Swin Transformer、Weka J48算法和模糊逻辑，在污水管网管道严重程度预测任务中，较仅图像分类提升了多项关键指标。
AI 中文摘要
标准自动化污水管道严重程度评估依赖直接图像分类，形成了视觉缺陷与最终严重程度评分之间关联不明确的“黑箱”。本研究提出一种模块化、基于模糊规则的神经符号框架，通过将神经感知与符号推理解耦来弥合这一差距。感知模块利用Swin Transformer直接从图像预测14个多标签检验CODE等级。推理环节采用Weka的J48算法（一种决策树DT），基于真实CODE和严重程度标签进行训练，其路径被转换为19条固定的IF-THEN规则。推理通过模糊逻辑运行：CODE条件的t-范数激活按规则置信度加权，并与对应的s-范数结合，生成可解释的类别证据。我们使用包含3244张图像的数据集评估了Product、Łukasiewicz和Hamacher算子对，该数据集涵盖5个高度不平衡的严重程度类别。真实标签通过5个独立大语言模型分析原始检验员笔记的共识稳健生成。结果显示，与仅基于图像的分类相比，本框架的准确率、平衡准确率、Macro F1和MCC分别提升了17.9%、12.2%、23.0%和17.3%。总体而言，该框架兼具具有竞争力的类别平衡性能，以及从预测的CODE等级到规则支持和严重程度证据的可追溯推理能力。
英文摘要
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.
