面向铁路周界远小目标入侵检测的视频超分辨率重建技术研究  

Research on Video Super-Resolution Reconstruction Technology for Intrusion Detection of Remote and Small Targets along Railway Perimeters

作　　者：王增卿 谢征宇[1,2,3] 姜忆玲 王佳丽 管岭 王力 WANG Zengqing;XIE Zhengyu;JIANG Yiling;WANG Jiali;GUAN Ling;WANG Li(School of Transportation,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Railway Industry for Operational Active Safety Assurance and Risk Prevention and Control,Beijing 100044,China;Beijing Urban Traffic Information Sensing and Service Technology Research Center,Beijing 100044,China;CSSC Systems Engineering Research Institute,Beijing 230027,China)

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]运营主动安全保障与风险防控铁路行业重点实验室,北京100044 [3]北京市城市交通信息智能感知与服务工程技术研究中心,北京100044 [4]中国船舶集团有限公司系统工程研究院,北京230027

出　　处：《中国铁道科学》2025年第1期200-211,共12页China Railway Science

基　　金：中央高校基本科研业务费专项资金资助项目(2024JBZY023)。

摘　　要：针对铁路长纵深监控环境下远小目标图像特征信息不足的问题,对视频超分辨率重建技术进行研究。首先,基于BasicVSR网络,利用前后帧信息设计特征交互增强(FIE)模块和图拉普拉斯金字塔低频分离(GLPLS)模块,构建高效的远小目标特征重建网络RailVSR;其次,在RailVSR网络中集成联合损失函数,优化网络对高分辨率图像和目标检测精度的双重关注;最后,将RailVSR网络与目标检测算法RT-DETR相结合,增强铁路长纵深监控场景下远小目标入侵检测能力。结果表明:与原始RT-DETR目标检测算法相比,基于RailVSR网络改进的目标检测算法对铁路周界入侵的检测精度至少提高13%,其平均检测精度至少提升11%;在VSTR铁路样本库构建的铁路监控数据集中,当目标像素占比超过0.05%时,检测漏报率和错报率均为0,且平均检测精度可达85%以上。该研究对铁路周界远小目标入侵检测具有有效性,可提升铁路安全运营的保障水平。Aiming at the problem of insufficient feature information of remote and small target images in railway long-depth monitoring scenarios,investigation on the video super-resolution reconstruction technology is conducted.Firstly,based on the BasicVSR network,the Feature Interaction Enhancement(FIE)module and the Graph Laplacian Pyramid Low-Frequency Separation(GLPLS)module are designed relying on the information from preceding and succeeding frames,thereby constructing an efficient feature reconstruction network RailVSR of remote and small targets.Secondly,a joint loss function is integrated into the RailVSR network to optimize the dual attention of network on high-resolution image quality and target detection precision.Lastly,by combining RailVSR network with the RT-DETR target detection algorithm,the capability for detecting remote and small target intrusions in the long-depth monitoring scenarios of railway is enhanced.The results demonstrate that compared with the original RT-DETR target detection algorithm,the improved algorithm based on the RailVSR network at least achieves a 13%improvement in the detection precision of railway perimeter intrusions,with an average precision increase of at least 11%.In the railway monitoring dataset constructed by the VSTR railway sample database,both the detection false negative rate and false positive rate are 0 when the proportion of target pixel exceeds 0.05%,and the average detection precision can reach over 85%.These findings confirm the effectiveness of the proposed method in intrusion detection of remote and small targets along railway perimeters,and significantly enhance the safety of railway operations.

关 键 词：铁路周界 远小目标 入侵检测 任务型视频超分辨重建 

分 类 号：U298[交通运输工程—交通运输规划与管理]