InfraGPT Smart Infrastructure：基于VLM的端到端城市缺陷检测与管理框架
InfraGPT Smart Infrastructure: An End-to-End VLM-Based Framework for Detecting and Managing Urban Defects
AI总结：
针对城市基础设施缺陷巡检依赖人工且现有系统输出零散的问题，提出结合YOLO检测分割与VLM的端到端框架，可识别多类缺陷并生成JSON结构化维修行动计划。
摘要：
智慧城市中的基础设施正越来越多地由闭路电视（CCTV）摄像头网络进行监控。道路、桥梁和隧道会出现裂缝、坑洼以及液体泄漏，这些问题威胁公共安全并需要及时修复。人工巡检成本高昂且具有危险性，而现有自动化系统通常只能处理单一缺陷类型，或提供无法直接指导维修人员的非结构化输出。本文提出一种综合流水线，利用街道CCTV视频流，通过YOLO系列目标检测器进行多缺陷检测与分割，并将检测结果传递给视觉语言模型（VLM）以生成具备场景感知能力的摘要。该VLM生成JSON格式的结构化行动计划，其中包括事件描述、推荐工具、尺寸、维修方案和紧急警报。本文回顾了有关坑洼、裂缝和泄漏检测的文献，重点介绍了QwenVL和LLaVA等大型视觉语言模型的最新进展，并描述了早期原型的设计。在公共数据集和采集的CCTV片段上进行的实验评估表明，该系统能够准确识别多种缺陷并生成连贯的摘要。最后，本文讨论了将系统扩展至全市范围部署所面临的挑战和方向。
英文摘要：
Infrastructure in smart cities is increasingly monitored by networks of closed circuit television (CCTV) cameras. Roads, bridges and tunnels develop cracks, potholes, and fluid leaks that threaten public safety and require timely repair. Manual inspection is costly and hazardous, and existing automatic systems typically address individual defect types or provide unstructured outputs that cannot directly guide maintenance crews. This paper proposes a comprehensive pipeline that leverages street CCTV streams for multi defect detection and segmentation using the YOLO family of object detectors and passes the detections to a vision language model (VLM) for scene aware summarization. The VLM generates a structured action plan in JSON format that includes incident descriptions, recommended tools, dimensions, repair plans, and urgent alerts. We review literature on pothole, crack and leak detection, highlight recent advances in large vision language models such as QwenVL and LLaVA, and describe the design of our early prototype. Experimental evaluation on public datasets and captured CCTV clips demonstrates that the system accurately identifies diverse defects and produces coherent summaries. We conclude by discussing challenges and directions for scaling the system to city wide deployments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
