Human intrusion detection for high-speed railway perimeter under all-weather condition

Purpose – The paper aims to solve the problem of personnel intrusion identification within the limits of highspeed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy ofobject recognition in dark and harsh weather conditions.Design/methodology/approach – This paper adopts the fusion strategy of radar and camera linkage toachieve focus amplification of long-distance targets and solves the problem of low illumination by laser lightfilling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm formulti-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposesa linkage and tracking fusion strategy to output the correct alarm results.Findings – Simulated intrusion tests show that the proposed method can effectively detect human intrusionwithin 0–200 m during the day and night in sunny weather and can achieve more than 80% recognitionaccuracy for extreme severe weather conditions.Originality/value – (1) The authors propose a personnel intrusion monitoring scheme based on the fusion ofmillimeter wave radar and camera, achieving all-weather intrusion monitoring;(2) The authors propose a newmulti-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring underadverse weather conditions;(3) The authors have conducted a large number of innovative simulationexperiments to verify the effectiveness of the method proposed in this article.

YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.

Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.

Electromagnetic Tomography System for Defect Detection of High-Speed Rail Wheel

A novel electromagnetic tomography(EMT)system for defect detection of high-speed rail wheel is proposed,which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils.A U-shaped sensor array was designed,and then a simulation model was built with the low frequency electromagnetic simulation software.Three different algorithms were applied to perform image reconstruction,therefore the defects can be detected from the reconstructed images.Based on the simulation results,an experimental system was built and image reconstruction were performed with the measured data.The reconstructed images obtained both from numerical simulation and experimental system indicated the locations of the defects of the wheel,which verified the feasibility of the EMT system and revealed its good application prospect in the future.

Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology

With the rapid development of urban rail transit,passenger traffic is increasing,and obstacle violations are more frequent,and the safety of train operation under high-density traffic conditions is becoming more and more thought provoking.In order to monitor the train operating environment in real time,this paper first adopts multisensing technology based on machine vision and lidar,which is used to collect video images and ranging data of the track area in real time,and then it performs image preprocessing and division of regions of interest on the collected video.Then,the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles.Finally,according to the danger degree of obstacles,determine the degree of impact on the train operation,and use the signal system automatic response ormanual response mode to transmit the detection results to the corresponding train,so as to control the train operation.Through simulation analysis and experimental verification,the detection accuracy and control performance of the detection method are confirmed,which provides safety guarantee for the train operation.

An Application of Canny Edge Detection Algorithm to Rail Thermal Image Fault Detection

The paper discusses an application for rail track thermal image fault detection. In order to get better results from the Canny edge detection algorithm, the image needs to be processed in advance. The histogram equalization method is proposed to enhance the contrast of the image. Since a thermal image contains multiple parallel rail tracks, an algorithm has been developed to locate and separate the tracks that we are interested in. This is accomplished by applying the least squares linear fitting technique to represent the surface of a track. The performance of the application is evaluated by using a number of images provided by a specialised company and the results are essentially favourable.

Application of improved back-propagation algorithms in classification and detection of scars defects on rails surfaces

An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.

Improved Roberts operator for detecting surface defects of heavy rails with superior precision and efficiency

An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects show that the improved Roberts operator can attain accurate positioning to defect contour and get complete edge information.Meanwhile,a decreasing amount of interference noises as well as more precise characteristic parameters of the extracted defects can also be confirmed for the improved algorithm.Furthermore,the BP neural network adopted for defects classification with the improved Roberts operator can obtain the target training precision with 98 iterative steps and time of 2s while that of traditional Roberts operator is 118 steps and 4s.Finally,an enhanced defects identification rate of 13.33%has also been confirmed after the Roberts operator is improved.The proposed detecting platform will be positive in producing high-quality heavy rails and guaranteeing the national transportation safety.

Investigation of monitoring system for high-speed railway subgrade frost heave

This paper presents methods for monitoring frost heave, device requirements, testing principals, and data analysis re- quirements, such as manual leveling observation, automatic monitoring （frost heave, frost depth, and moisture）, track dynamic detection, and track status detection. We focused on the requirements of subgrade frost heave monitoring for high speed railways, and the relationship of different monitoring methods during different phases of the railway. The com- prehensive monitoring system of high speed railway subgrade frost heave provided the technical support for dynamic design during construction and safe operation of the rail system.

基于tSNE多特征融合的JTC轨旁设备故障检测

无绝缘轨道电路(Jointless Track Circuit,JTC)的轨旁设备在室外长期运营过程中,其可靠性会逐渐降低,进而给列车行车安全带来严重威胁。以轨道电路读取器(Track Circuit Reader,TCR)感应电压为基础,针对JTC故障诊断研究中轨旁设备故障类型复杂和故障特征提取不充分等问题,提出一种基于t分布随机邻域嵌入(t-distribution Stochastic Neighbor Embedding,tSNE)多特征融合的JTC轨旁设备故障检测模型。首先,根据不同轨旁设备故障对TCR感应电压信号的影响,分析各轨旁设备的故障特性。其次,提取TCR感应电压信号的方差、有效值、峰值因子等幅值域特征,以及排列熵、散布熵特征构成原始故障特征集。为了去除其中的冗余信息,得到具有较高判别性的融合流形特征,利用tSNE算法进行特征融合。最后输入深度残差网络(Deep Residual Network,DRN)得到故障检测混淆矩阵,实现轨旁设备故障定位。实验结果表明:tSNE算法融合后的特征在异类和同类故障样本之间分别有较大的类间间距和较小的类内间距,相比主成分分析(Principal Component Analysis, PCA)、随机相似性嵌入(Stochastic Proximity Embedding, SPE)、随机邻域嵌入(Stochastic Neighbor Embedding,SNE)算法具有更优的融合特征提取效果。此外,结合DRN可以有效识别多种轨旁设备故障,达到98.28%的故障检测准确率。通过现场信号进行实例验证,结果表明该故障检测模型能满足铁路现场对室外设备进行故障定位的实际需求。

基于视频的轨道车辆自主定位方法研究

针对轨道施工车辆自主定位需求,提出一种基于车载前视相机拍摄百米标视频的轨道车辆自主定位方法。该方法首先对YOLOX-s网络进行改进并构建了百米标的目标检测模型,完成对百米标的目标检测;其次,当检测到百米标后,结合图像预处理和卷积循环神经网络(CRNN)网络构建百米标数字文本识别模型,提取百米标的数字文本信息,从而实现对轨道施工车辆的定位。经实验验证该方法能够快速准确定位轨道施工车辆的位置信息。

基于深度视觉算法的轨面伤损检测方法

针对现有目标检测器存在的推理延迟、不稳定和高计算成本等问题,提出一种基于深度学习理论的创新算法RT-DETR(RT-DETR-L),实现了对钢轨表面伤损的高效精细化检测。基于该算法设计的目标检测实验方案,去除了传统目标检测算法中的非极大值抑制(NMS)后处理步骤;引入了一个解耦单尺度内部交互和跨尺度融合的高效混合编码器;提出了一种IoU-aware初始化对象查询机制,并重新定义了目标函数。实验结果表明,该方案能有效提高算法在检测钢轨表面伤损时的准确率和召回率,在检测剥离掉块、疲劳裂纹、接头方面表现出色,准确率分别为95.1%、93.8%和99.5%,检测速度为8.62 ms/帧,参数量仅为4.2 M。该研究成果能够为钢轨养护维修提供一种准确高效的检测方案。

基于深度学习的轨道交通通信系统数据异常智能检测

为了防止黑客利用技术漏洞攻击轨道交通通信系统,导致严重的轨道交通事故。研究了基于深度学习的神经网络算法轨道交通车辆通信系统入侵检测系统(IDS),并利用梯度下降动量(GDM)和自适应增益(GDM/AG)来提高IDS的效率和准确性,并通过使用真实的轨道车辆对所提出模型的准确性和效率进行了验证和评估。实验表明,与GDM算法相比,GDM/AG算法在轨道车辆异常检测中可以实现更快的收敛,并且可以检测到毫秒级的异常数据。同时,提出的模型可以自适应检测未知的攻击,在面对未知攻击类型时,其准确率及精度均达到98%以上。

城市轨道交通系统的层次化功能结构解析——以上海为例

解析城市轨道交通系统的功能结构对于建立以轨道交通为骨架的城市空间结构至关重要,其有助于优化城市空间布局,促进交通与土地利用融合,进而推动城市可持续发展。本文利用交通刷卡大数据,基于轨道交通站域的功能相似性和邻接关系提出了功能站组的概念,并形成了一套“站域功能分类—站组范围划定—站群结构识别”的方法体系。以上海市轨道交通系统为例,针对单个站域,构建表征站域土地利用功能的连续客流时间序列,并依据时间序列特征分类得到站域功能类型;将多个具有相似的出行模式和土地利用功能的相邻站域划定为功能站组;以功能站组为基本单元,采用社区发现算法,对功能站组间的客流交互网络进行分析以识别站群。研究结果表明,城市轨道交通系统的“站域—站组—站群”层次化功能结构解析方法综合了场所空间和流空间视角,有助于认识特大城市轨道交通系统的功能结构特征,并为轨道交通系统的发展提供多层次的空间优化建议。

面向轨道智能交通大学生创新训练实践教学平台设计

以轨道智能交通中的入侵异物智能检测为对象,设计了一种新型的大学生创新训练实践教学平台,提出了一种基于深度学习的轨道异物入侵检测模型,并结合人工智能和机械设计等交叉学科专业知识,自主研发了一种小型轨道异物入侵智能检测系统物理样机,实现了复杂环境下的高效轨道异物入侵检测。实验和实践结果表明,该模型很好地平衡了检测速度和精度,在NVIDIA GTX1080Ti平台上对自建轨道异物入侵数据集的平均检测精度为96.1%,检测速度为209 FPS。通过上述大创实践教学平台的设计、实施和考核,培养和激发了大学生的创新实践和团队协作能力。

城市轨道交通新型检测技术及应用浅析

城市轨道交通的高质量发展对检测技术的安全性、可靠性、实时性、标准化和集成化等提出了更高的要求。激光检测技术、机器视觉检测技术、超声检测技术和红外检测技术等新型技术的出现和应用,很好地弥补了传统检测技术的不足,满足了城市轨道交通现阶段检测技术的需求。双轨机器人、检测管理平台、主动预警系统及非接触检测等方法在不同场景应用,给城市轨道交通新型检测技术的推广提供了支持。本文通过综合分析,预测综合性、基于信息化、智能化、主动检测及动态与非接触检测相结合等将成为城市轨道交通新型检测技术的方向发展。

稀疏可变形卷积与高分辨率融合的接触网螺栓病害检测

列车长期运行产生的震动易导致接触网螺栓处于松动、脱落等不良状态,接触网取流异常会严重影响行车安全。针对高速铁路接触网螺栓病害检测时,易受复杂背景干扰及螺栓松动病害难以检测等问题,提出一种稀疏可变形卷积与高分辨率融合的接触网螺栓病害检测方法。首先,构建稀疏动态可变形卷积构成的特征提取网络,通过增大感受野范围,来捕捉不同尺度下螺栓的形状特征,加强模型对螺栓小尺寸对象特征的提取能力。然后,设计高分辨率特征金字塔融合模块,将螺栓深层特征和浅层特征的高分辨率特征图进行充分融合,提高多尺度特征图的利用率。其次,提出基于连通域统计的螺栓松动判别方法,通过统计被截断螺栓的连通域个数,完成螺栓松动病害状态检测。最后,由高速铁路接触网螺栓检测试验得出:所提方法可以准确检测螺栓的缺失和松动病害,且具有较高的检测精度,相比改进前Mask R-CNN检测方法准确率增加了41.4个百分点、召回率增加了27.3个百分点、像素精确度提升28.11个百分点、F1-score达83.4%。同时,对接触网螺栓网络模型的检测效率进行试验,较Mask R-CNN的浮点计算效率提升了36.23%。对不同场景下接触网螺栓检测对比试验表明,所提方法具有良好的适应性和精确度,对于螺栓松动和缺失病害检测提供了更为准确的方法,对后期接触网智能化检测具有一定的参考意义。

基于机器视觉的钢轨表面面型缺陷分类实验设计

随着城市轨道交通的飞速发展,实现钢轨表面缺陷实时检测对铁路行业稳步发展意义重大。如何实时检测钢轨表面缺陷是保障铁路运行安全亟须解决的一个关键问题。鉴于此,设计了一套基于机器视觉的钢轨表面缺陷检测实验仿真方法。搭建图像采集、图像预处理和缺陷分类等模块;提出自拟合亮度调整算法完成像素值统计,得到清晰的缺陷特征图像;用750组数据训练网络权值,实现缺陷分类预测;经过数据分析和误差评估,识别准确率在90%以上,相关系数高达0.96,单幅图像平均耗时1.267 s,测试表明,所提方法能准确、高效地实现钢轨表面缺陷信息的缺陷分类与识别。

基于纹理特征增强的重载铁路钢轨缺陷检测算法

为实现重载铁路轨道典型缺陷的准确、快速、智能检测,基于深度学习算法,提出一种针对重载铁路钢轨图像的特征加强卷积神经网络模型,研制一套基于机器视觉的便携式轨道图像采集系统;整理创建重载铁路钢轨表面多目标图像数据集,并基于此数据集进行训练,实现裂纹、擦伤、块状损伤、接缝4种典型缺陷目标的智能识别;针对数据集中目标尺度分布不平衡的特点,使用聚类算法重新设置锚框的尺寸和数量;对比分析重载铁路钢轨缺陷图像的纹理复杂性与固有特点,引入加权融合池化模块和纹理特征增强模块对自适应训练样本选择(ATSS)算法进行改进。应用所提算法对重载铁路轨道进行检测,4类典型缺陷目标的全类平均正确率达到85.8%。通过与其他9种检测算法的对比,充分验证了所提算法的有效性。