城市哨兵：基于AI的统一智能监控框架，用于使用深度学习的实时多威胁检测
City Sentinel: A Unified AI-Based Smart Surveillance Framework for Real-Time Multi-Threat Detection Using Deep Learning
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中文总结 AI 辅助
该研究提出City Sentinel统一监控框架，集成六种检测功能，采用多模型架构，在低延迟下实现高准确率，可灵活扩展新功能，满足实时多威胁监控需求。
中文摘要 AI 辅助
快速的城市化增加了对可同时监控多种公共安全风险的监控系统的需求。传统系统通常使用单独的解决方案来进行人脸识别、车辆识别、火灾检测和行为分析，导致基础设施碎片化，操作员需要管理多个界面。本文提出了City Sentinel，一个基于AI的统一监控框架，将六种检测功能集成到一个可扩展平台中：人脸识别、自动车牌识别（ANPR）、火灾和烟雾检测、武器和刀具检测、暴力行为检测以及道路事故检测。该系统结合了operator仪表板、FastAPI后端、基于云的PostgreSQL事件存储、用于人脸识别的InsightFace和YOLOv8视觉模型，以及用于车牌识别的EasyOCR。摄像头流通过专用推理工作进程使用RTSP进行处理。在配备NVIDIA RTX 3060 GPU的工作站上，该系统实现了743毫秒的中位数端到端延迟，并且在两秒延迟限制内支持四路并发RTSP流。它实现了91.2%的人脸匹配率、85.7%的车牌读取准确率，以及火灾、刀具和武器检测模块的mAP@0.5分数在0.846至0.889之间。在用户接受度测试中，操作员可以在一分钟内注册新身份，平均12秒内从实时 footage中识别出标记人员。结果表明，模块化、开源、多模型架构可提供广泛的监控覆盖范围、基于云的可审计性，以及添加新检测功能的灵活性，同时保持实用的实时性能。
英文摘要
Rapid urbanization has increased the need for surveillance systems that can monitor multiple public safety risks at the same time. Traditional systems often use separate solutions for facial recognition, vehicle identification, fire detection, and behavioral analysis, resulting in fragmented infrastructure and multiple interfaces for operators to manage. This paper presents City Sentinel, a unified AI-based surveillance framework that integrates six detection capabilities into one scalable platform: facial recognition, automatic number plate recognition (ANPR), fire and smoke detection, weapon and knife detection, violence detection, and road accident detection. The system combines a Next.js operator dashboard, FastAPI backend, cloud-based PostgreSQL event storage, InsightFace and YOLOv8 vision models, and EasyOCR for plate recognition. Camera streams are processed through dedicated inference workers using RTSP. On a workstation equipped with an NVIDIA RTX 3060 GPU, the system achieves a median end-to-end latency of 743 ms and supports four concurrent RTSP streams within a two-second latency limit. It achieves a 91.2% face-match rate, 85.7% plate-reading accuracy, and mAP@0.5 scores of 0.846 to 0.889 across the fire, knife, and weapon detection modules. In user-acceptance testing, operators could enroll a new identity in under one minute and identify a flagged person from live footage in an average of 12 seconds. The results demonstrate that a modular, open-source, multi-model architecture can provide broad surveillance coverage, cloud-based auditability, and flexibility for adding new detection capabilities while maintaining practical real-time performance.
发表机构
- GIK Institute（GIK学院）
机构由 AI 辅助整理，请以论文原文为准。
