基于B/S架构的多路火焰智能监控系统设计

The Design of Multi⁃Channel Flame Intelligent Monitoring System Based on B/S Architecture

班铭 田鹏伟 曹江涛

Ming BAN Pengwei TIAN Jiangtao CAO

辽宁石油化工大学 信息与控制工程学院，辽宁 抚顺 113001

School of Information and Control Engineering，Liaoning Petrochemical University，Fushun Liaoning 113001，China

在化工厂这类高风险地区，火灾安全始终备受关注。尽管烟雾及火焰报警器已被广泛应用，但仍然存在只用于单点检测以及易受环境影响等问题。针对此类问题，设计并实现了一种基于B/S架构的化工厂区多路火焰智能视频监控系统，并以Web系统的形式呈现。首先，系统中集成了一种改进的YOLOv5火焰检测算法，该算法用Ghost卷积替换常规卷积以实现网络的轻量化；其次，添加改进的注意力机制模块和小目标检测锚框来增强小目标检测能力；最后，将由光流网络提取的火焰运动信息与原始火焰数据进行融合送入改进的YOLOv5火焰检测算法中，进一步提高对火焰的检测精度。大量的现场测试表明，该系统能够实时识别和定位厂区内火焰，检测帧率可达15 ms/帧，检出率达到100%，具有较高的稳定性，可为化工行业提供一种高效、可靠的火灾监测解决方案。

As a high?risk area, the fire safety has always attracted much attention. Although smoke and flame alarm have been widely used, there are still problems such as single ?point detection and easy environmental impact. In response to such problems, a multi?way flame smart video monitoring system based on the B/S architecture is designed and implemented, and it is presented in the form of a web system. In the system, an improved YOLOV5 flame detection algorithm is integrated. The Ghost convolution is used to replace in conventional convolution to achieve the lightweight of the network, and the improved attention mechanism modules and small target detection anchor frame is added to enhance small target detection ability. Finally, the flame movement information extracted from the Optical flow network and the original flame data is sent into the improved YOLOV5 flame detection algorithm to further improve the detection accuracy of the flame. A large number of on?site test proves that the system can identify and locate the flames in the plant in real time. The detecting frame rate can reach 15 ms/frame, and the detection rate reaches 100%, which has high stability. An efficient and reliable fire monitoring solution is provided for the chemical industry.

国家自然科学基金项目(61673199)

10.12422/j.issn.1672-6952.2025.02.011