基于改进ByteTrack的高铁周界入侵监测方法研究

Research on intrusion detection of high speed railway perimeter based on the improved ByteTrack

本文旨在应对高铁周界环境复杂、小目标多等情况,研究周界入侵行为的识别与跟踪问题,并提出一种改进ByteTrack算法。本文融合YOLOv7-X与BYTE数据关联方法对模型进行改进,并且引入卷积块注意力机制以提升周界复杂环境下前景目标的识别效果,利用空间-深度转化模块优化跨步卷积与池化层,改善小目标识别时下采样导致的细粒度信息丢失情况。制作铁路周界入侵数据集进行实验,实验结果表明,改进后的模型平均精度达到95.6%,提升了9.4%,对大中小目标识别的平均精度均有提升,尤其是对小目标识别效果提升显著,提升了22.2%。结果表明改进ByteTrack算法在高铁周界复杂环境下能实现入侵行为的识别与跟踪,为高铁周界防护提供技术支持。

To address the problems of high-speed railway perimeter intrusion detection such as complex surroundings and a large number of small targets,an improved ByteTrack algorithm is proposed to realize the identification and tracking of perimeter intrusion.The model is improved by integrating YOLOv7-X and the data association method of BYTE.The convolution block attention module is introduced to improve the recognition effect of foreground targets in complex surroundings.The space-to-depth layer and the non-strided convolution layer are used to optimize the step convolution and pooling layers to improve the loss of fine-grained information caused by down-sampling in small target recognition.The railway perimeter intrusion dataset is established for experiments.The experimental results show that the AP of the improved module is 95.6%,an increase of 9.4%,and has improved the AP of target recognition for large,small,and medium-sized targets,especially for small targets,with a significant improvement of 22.2%.The improved ByteTrack algorithm can realize the identification and tracking of intrusion behavior in the complex environment of high-speed railway perimeter,and provide technical support for high-speed railway perimeter protection.