[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4064":3,"related-4064":81},{"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":62,"slug":63,"view_count":15,"doi":64,"paper":65,"created_at":80},4064,"A Hybrid Single-Shot Vision-AI and Geospatial Framework for Multimodal Infrastructure Degradation Mapping in Urban Networks","https:\u002F\u002Fdoi.org\u002F10.51903\u002Fdza92157","Conventional transportation infrastructure inspection is often labor-intensive, time-consuming, and limited to individual asset types, reducing the efficiency of large-scale infrastructure management. This study proposes a hybrid Vision-AI and geospatial framework for automated multimodal infrastructure deterioration detection and digital asset monitoring. The framework integrates a YOLO-based object detection model, edge computing, geospatial synchronization, and a Digital Twin Web-GIS platform to detect, localize, and visualize infrastructure defects in real time. Experimental evaluation under multiple environmental conditions demonstrated stable detection performance, achieving 95.1% Precision, 94.3% Recall, 94.7% F1-score, and 96.4% mAP@0.5 under daylight conditions while maintaining reliable accuracy under shadowed, wet, and low-light environments. Edge deployment on the NVIDIA Jetson Orin Nano also provided low-latency inference suitable for real-time field applications. The novelty of this research lies in the integration of multimodal Vision-AI defect detection, edge computing, geospatial synchronization, and Digital Twin visualization within a unified framework capable of simultaneously monitoring heterogeneous transportation infrastructure assets. This study contributes by addressing the research gap between AI-based inspection and spatial asset management while providing an operational framework for automated maintenance decision support. The results demonstrate that integrating artificial intelligence with geospatial technologies can improve the accuracy, efficiency, and scalability of intelligent transportation infrastructure inspection and management.","传统交通基础设施巡检通常劳动密集、耗时且局限于单一资产类型，降低了大范围基础设施管理的效率。本研究提出了一种融合视觉人工智能与地理空间的混合框架，用于自动化多模态基础设施劣化检测与数字资产监测。该框架集成了基于YOLO的目标检测模型、边缘计算、地理空间同步以及数字孪生Web-GIS平台，以实时检测、定位并可视化基础设施缺陷。在多种环境条件下的实验评估表明，检测性能稳定，在日光条件下达到95.1%的精确率、94.3%的召回率、94.7%的F1分数和96.4%的mAP@0.5，同时在阴影、潮湿和低光照环境中保持可靠精度。在NVIDIA Jetson Orin Nano上的边缘部署还提供了适用于实时现场应用的低延迟推理。本研究的新颖性在于将多模态视觉人工智能缺陷检测、边缘计算、地理空间同步和数字孪生可视化集成于一个统一框架中，能够同时监测异构交通基础设施资产。本研究通过弥合基于人工智能的巡检与空间资产管理之间的研究空白，并为自动化维护决策支持提供可操作框架而作出贡献。结果表明，将人工智能与地理空间技术相结合，可以提高智能交通基础设施巡检与管理的准确性、效率和可扩展性。",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},"该论文聚焦城市交通基础设施缺陷检测，与三农、农业信息化、智慧农业等主题无关，不予入选。",[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":61},[28,39,47,54],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":38},"I2",9,true,[],[34,35,36,37],"目标检测","artificial intelligence","YOLO","decision support",[],{"code":40,"score":41,"supported":31,"title":42,"abstract":43,"tags":46},"I1",6,[],[44,45],"数字孪生","digital twin",[],{"code":48,"score":41,"supported":31,"title":49,"abstract":50,"tags":53},"I5",[],[51,52],"边缘计算","edge computing",[],{"code":55,"score":56,"supported":31,"title":57,"abstract":58,"tags":60},"I4",3,[],[59],"GIS",[],"农业相关性需要复核",[],"AHybridSingle-ShotVision-AIandGeospatial-4064","10.51903\u002Fdza92157",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":15,"oa_url":72,"card":73,"direction":77,"ingested_from":79},"W7215031713",[68,70],{"name":69,"orcid":9},"Mikkel Sørensen",{"name":71,"orcid":9},"Freja Holm","https:\u002F\u002Fcest.stekom.ac.id\u002Findex.php\u002Fcest\u002Farticle\u002Fdownload\u002F164\u002F36",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"提出融合YOLO、边缘计算与数字孪生的框架，实现城市交通基础设施缺陷实时检测与可视化。","YOLO目标检测、Jetson边缘计算、地理空间同步与数字孪生Web-GIS平台","白天检测达95.1%精度、94.7% F1，阴影湿滑低光下仍稳定，边缘端低延迟。","智慧农业 \u002F 农业物联网","可将该视觉-地理-数字孪生框架迁移至农田道路、灌排设施等乡村基础设施巡检与资产管理。","openalex","2026-10-03T23:30:11.471604Z",{"total":15,"page":82,"page_size":82,"items":83},1,[]]