Intelligent vision based personnel intrusion prevention system for railway bridge
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摘要: 铁路桥梁监测是保障铁路运输安全的重要手段。为提升现有监测系统对铁路大桥人员入侵的检测能力，设计了基于智能视觉的铁路大桥人员入侵防护系统，该系统由视频平台、智能视觉平台及业务管理平台组成。采用YOLOv5目标检测模型进行人员入侵检测；同时，采用多种图像数据增强技术，扩增训练数据集，进一步提升目标检测模型的泛化能力和场景适应能力。在包神铁路集团有限公司万南站区黄河大桥对该系统进行了部署和测试。测试结果表明，该系统对人员入侵检测的准确率为95.3%，检测实时性为2 ms；人员入侵检测的准确率与实时性均满足实际应用要求。Abstract: Railway bridge monitoring is an important means to ensure transportation safety. To enhance the detection capability of existing monitoring systems for railway bridge personnel intrusion, this paper designed a personnel intrusion prevention system for railway bridge that included a video platform, an intelligent visual platform, and a business management platform. The paper adopted the YOLOv5 object detection model for personnel intrusion detection, and adopted multiple image data enhancement technologies to expand the training dataset, further improve the generalization ability and scene adaptation ability of the object detection model. The system was deployed and tested at the Yellow River Bridge in the Wannan Station area of Baoshen Railway Group Limited Liability Company. The experimental results show that the detection accuracy of the system for personnel intrusion is 95.3%, and the real-time detection performance is 2 ms. The accuracy and real-time performance of personnel intrusion detection meet the practical application requirements.
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表 1 铁路大桥人员入侵防护系统服务器参数
处理器 Intel Xeon Silver 4214R 2.4G/12Core/16.5M/100W/Tray 内存模块 32GB DDR4-2933 ECC REG 存储模块 Seagate ST2000NM0008 2TB/128M/7200RPM/SATA/3.5" GPU NVIDIA Tesla T4 GPU 75W 16GB LP Passive 900-2G183-0000-00 网络 双万兆以太网端口 操作系统 Ubuntu18.04 
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表 2 实验环境
环境配置 名称 信息 硬件配置 GPU NVIDIA TITAN V CPU Intel Core i7-11800H 内存 16G 显存 12G 软件环境 操作系统 Ubuntu18.04 Python 3.8.0 Pytorch 1.8.0 CUDA 11.1 cuDNN 8.1.0
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表 3 超参数设置
名称 数值 输入图像分辨率 640×640×3 迭代运行次数 300 批处理大小 4 优化器 SGD 初始学习率 0.01 周期学习率 0.01 学习率动量 0.99 权重衰减系数 0.0001
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