一个端到端的人工智能物 (AIoT) 解决方案,用于保护管道方便防止外部干扰-一个澳大利亚的使用案例
Umair Iqbal1, Johan Barthelemy1, Guillaume Michal2
1SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2500, Australia.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究介绍了一种使用计算机视觉检测管道威胁的AIoT系统,以实时检测管道威胁. 在10公里的管道上部署,它提高了危险物质运输的安全性.
科学领域:
- 管道的完整性和安全性以及管道的安全性.
- 事物的人工智能 (AIoT)
- 计算机视觉用于威胁检测和检测.
背景情况:
- 运输危险物质的高压管道容易受到第三方干扰.
- 现有的预防措施不足,需要先进的实时检测策略.
- 由干扰造成的事故对基础设施和环境构成重大风险.
研究的目的:
- 开发和部署一个端到端的AIoT解决方案,用于实时检测管道干扰威胁.
- 创建一个智能视觉传感器系统,利用计算机视觉算法.
- 为管道运营商提供及时的警报,以便他们立即作出反应.
主要方法:
- 开发一个具有边缘计算能力的智能视觉传感器.
- 在定制的管道视觉威胁评估 (Pipe-VisTA) 数据集上训练物体检测模型 (YOLOv4,DINO).
- 在NVIDIA Jetson Nano上部署一个TensorRT优化的YOLOv4模型,用于实时边缘处理.
- 与LoRaWAN和卫星网络集成,用于报警传输和仪表板托管.
主要成果:
- 在一个看不见的测试数据集上,DINO模型实现了71.2%的平均精度 (mAP).
- 优化为边缘部署的YOLOv4模型在测试数据集上实现了61.8%的mAP.
- 成功地进行了现场测试,并在SEA天然气管道的10公里长段部署了48个AIoT设备.
结论:
- 开发的AIoT解决方案有效地实时检测潜在的管道威胁.
- 用优化模型,如YOLOv4进行边缘计算,对于资源有限的设备是可行的.
- 该系统增强了管道安全和对危险物质运输的运营意识.
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