[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4066":3,"related-4066":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":53,"slug":54,"view_count":15,"doi":55,"paper":56,"created_at":72},4066,"AI-Driven Computer Vision for Pavement Distress Classification and Structural Degradation Mapping in Urban Highway Networks","https:\u002F\u002Fdoi.org\u002F10.51903\u002F30q9ch55","Timely pavement condition assessment is essential for maintaining road safety and optimizing maintenance planning; however, conventional inspection methods remain labor-intensive, time-consuming, and often lack the capability for continuous large-scale monitoring. This study aims to develop an artificial intelligence-based pavement monitoring framework that integrates real-time pavement distress detection, Pavement Condition Index (PCI) estimation, and Geographic Information System (GIS)-based spatial visualization into a unified decision-support platform. The proposed framework employs a YOLO-based deep learning model for automated detection of multiple pavement distress types using vehicle-mounted camera imagery, followed by PCI computation and geospatial degradation mapping to support network-level pavement assessment. Experimental evaluation demonstrated that the proposed model achieved a composite precision of 0.923, recall of 0.881, mean Average Precision (mAP) of 0.769, and an average inference time of 8.4 ms, indicating its capability to perform accurate real-time pavement distress detection under diverse urban road conditions. The novelty of this research lies in the integration of AI-based pavement distress detection, automated PCI estimation, and GIS-based structural degradation mapping within a single low-cost vehicle-mounted inspection framework, enabling simultaneous condition assessment and spatial infrastructure management without relying on expensive inspection technologies. This integrated workflow provides a practical contribution by supporting maintenance prioritization, reducing inspection time, and improving data-driven pavement management for transportation agencies. The results demonstrate that the proposed framework offers an effective and scalable solution for intelligent pavement monitoring while extending the application of computer vision beyond object detection toward comprehensive infrastructure condition assessment and maintenance decision support.","及时的路面状况评估对于维护道路安全和优化养护规划至关重要；然而，传统检测方法仍然劳动密集、耗时，且往往缺乏持续大规模监测的能力。本研究旨在开发一个基于人工智能的路面监测框架，将实时路面病害检测、路面状况指数（Pavement Condition Index, PCI）估算以及基于地理信息系统（Geographic Information System, GIS）的空间可视化集成到一个统一的决策支持平台中。所提出的框架采用基于YOLO的深度学习模型，利用车载摄像头图像自动检测多种路面病害类型，随后进行PCI计算和地理空间退化制图，以支持路网级路面评估。实验评估表明，所提出模型达到了0.923的综合精确率、0.881的召回率、0.769的平均精度均值（mean Average Precision, mAP）以及8.4 ms的平均推理时间，表明其能够在多样化的城市道路条件下进行准确的实时路面病害检测。本研究的新颖性在于将基于人工智能的路面病害检测、自动化PCI估算和基于GIS的结构退化制图集成在一个单一的低成本车载检测框架内，无需依赖昂贵的检测技术即可同时实现状况评估和空间基础设施管理。这一集成工作流程通过支持养护优先级排序、减少检测时间并改善交通机构的数据驱动路面管理，提供了实际贡献。结果表明，所提出的框架为智能路面监测提供了一种有效且可扩展的解决方案，同时将计算机视觉的应用从目标检测拓展至全面的基础设施状况评估和养护决策支持。",null,"Civil Engineering Science and Technology","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦城市公路路面病害的AI视觉检测与GIS退化制图，属于交通基础设施养护领域，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":52},[28,43],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":42},"I2",17,true,[33],"computer vision",[35,36,37,38,39,33,40,41],"目标检测","深度学习","计算机视觉","deep learning","artificial intelligence","YOLO","decision support",[],{"code":44,"score":45,"supported":31,"title":46,"abstract":47,"tags":51},"I4",9,[],[48,49,50],"地理信息","GIS","geographic information",[],"农业相关性需要复核",[],"AI-DrivenComputerVisionforPavementDistre-4066","10.51903\u002F30q9ch55",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":15,"oa_url":63,"card":64,"direction":70,"ingested_from":71},"W7215059544",[59,61],{"name":60,"orcid":9},"Bram van Loon",{"name":62,"orcid":9},"Elise Verhoeven","https:\u002F\u002Fcest.stekom.ac.id\u002Findex.php\u002Fcest\u002Farticle\u002Fdownload\u002F162\u002F35",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"提出YOLO车载视觉框架，实现路面病害实时检测、PCI估算与GIS退化制图。","YOLO深度学习+车载相机图像，结合PCI计算与GIS空间可视化。","模型精度0.923、召回0.881、mAP 0.769，推理8.4ms，可实时低成本评估路面。","其他","可将该低成本车载视觉+GIS框架迁移至农村公路或田间道路监测，结合农机轨迹数据拓展农业基础设施管理。","智慧农业 \u002F 农业物联网","openalex","2026-10-03T23:30:11.692304Z",{"total":15,"page":74,"page_size":74,"items":75},1,[]]