基于红外图像识别技术的隧道防火预警巡检方法

Tunnel Fire Warning Inspection Method Based on Infrared Image Recognition Technology

为了提高隧道火灾预防能力,基于红外图像识别技术设计了一套隧道防火预警系统。硬件部分采用红外CCD摄像头、红外光源和红外滤波光片,实现了红外图像的采集。利用树莓派4代开发板,对采集到的红外图像进行实时处理与识别,利用报警控制主机,完成隧道的防火预警。软件部分使用Python编程语言和模块化的思想,采用迁移学习和基于区域的全卷积网络算法,实现了红外图像的识别。试验结果表明,红外图像识别准确率可高达96%,系统运行12 h时,消耗的功率为100 W,红外图像识别准确率高。所设计的系统为下一步技术研究奠定了基础。

In order to improve the ability of tunnel fire prevention,a tunnel fire early warning system was designed based on infrared image recognition technology.The hardware part adopts an infrared CCD camera,an infrared light source and an infrared filter to realize the acquisition of infrared images.Raspberry Pie 4 Generation Development Board was used to process and identify the collected infrared images in real time,and the alarm control host was used to complete the fire warning of tunnels.The software part used the Python programming language and the idea of modularization,adopted transfer learning and a region-based fully convolutional network algorithm,and realized the recognition of infrared images.The test results show that the accuracy of infrared image recognition can be as high as 96%.When the system runs for 12 h,the power consumption is 100 W,and the accuracy of infrared image recognition is high.The designed system lays the foundation for the next technological research.