铁路异物侵限在线监测技术是铁路运行安全和旅客生命财产安全的重要保障.针对当前异物检测算法存在的遮挡目标检测、小目标检测不全不准等问题,提出一种基于YOLOv8的铁路异物检测算法Vanilla-YOLOv8.首先,结合VanillaNet中减少网络深度、短接分支以及通过改变深度训练策略和动态调节激活函数状态增强非线性能力的思想,改善YOLOv8模型中由于网络层数过多、短接分支过盛带来的模型退化、耗时以及深层小目标特征消失等问题,提高模型的特征提取能力和检测速度;然后,利用改进的局部卷积减少冗余特征的出现,确保特征能被充分利用;最后,在网络主干部分融入压缩激励(Squeeze-and-Excitation,SE)注意力机制,提高网络中关键特征的权重,加强对遮挡目标和小目标的特征表征能力和检测能力.实验结果表明:Vanilla-YOLOv8 算法的平均精度均值达到 98.7%,参数量下降 61.39%,识别速度达到 125 帧每秒(Frames Per Second,FPS),速度和检测精度较传统的图像处理技术有较大提升.研究结果可以为在线实时监测提供参考.
Vanilla-YOLOv8 railway foreign object intrusion detection method based on feature redundancy reduction
Online monitoring technology for railway foreign object intrusion plays a critical role in ensur-ing the safety of railway operations and the security of passengers'lives and property.To address the issue of incomplete and inaccurate detection of occluded targets and small targets in existing foreign ob-ject detection algorithms,this paper introduces a railway foreign object detection algorithm,Vanilla-YOLOv8,based on YOLOv8.First,leveraging VanillaNet's approach of reducing network depth,shortcut branches,and enhancing nonlinear capabilities through deep training strategy modifications and dynamic adjustment of activation function states,the proposed method mitigates problems like model degradation,time inefficiency,and the disappearance of low-level small target features caused by excessive network layers and shortcut branches.This enhances the model's feature extraction capa-bility and detection speed.Then,improved partial convolution is employed to reduce redundant fea-tures,ensuring optimal utilization of extracted features.Finally,a Squeeze-and-Excitation(SE)atten-tion mechanism is integrated into the network backbone to increase the weight of key features,enhanc-ing feature representation and detection capabilities for occluded and small targets.Experimental re-sults show that the Vanilla-YOLOv8 algorithm achieves the mean average precision of 98.7%,re-duces parameters by 61.39%,and reaches the recognition speed of 125 Frames Per Second(FPS).These improvements mark a substantial advancement over traditional image processing techniques in terms of speed and detection accuracy,offering a valuable reference for real-time online monitoring.
万方数据
维普
