基于改进支持向量机的图像识别技术在轨道紧固件异常监测中的应用研究

Xianzheng Fan, Xiongfeng Jiao, Mingming Shuai, Yi Qin, Jun Chen
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引用次数: 0

摘要

铁路运输是人们的主要交通工具,也是物流运输的主要方式,在人们的日常生活中起着重要的作用。因此,铁路轨道安全检测受到了广泛的重视。钢轨扣件异常智能检测是钢轨安全检测的关键内容。传统的钢轨扣件检测方法是基于SVM等机器学习图像识别来检测异常钢轨扣件。但传统方法存在两个缺陷。第一点是检测时间长,第二点是检测精度低。针对这一问题,提出了一种基于IFOA算法优化的支持向量机的钢轨扣件异常检测模型。首先对钢轨扣件图像进行采集和滤波;然后,进行边缘检测和图像分割,得到目标区域的图像;最后提取图像的HOG特征和LBP特征,利用改进的IFOA-SVM对特征进行识别和分类,从而实现智能轨道扣件异常检测。实验结果表明,当IACO-SVM模型迭代到254次时,适应度值趋于稳定,为0.24。该模型检测精度达到99.82%,高于传统模型,能够满足轨道扣件异常检测的工作要求。基于支持向量机的钢轨扣件异常检测模型可以提高钢轨扣件异常检测的效率,对铁路运输的正常运行具有积极作用。然而,由于研究中使用的实验样本数量有限,可能会导致实验结果出现一些误差。因此,在后续的研究中,有必要增加样本数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application research of image recognition technology based on improved SVM in abnormal monitoring of rail fasteners
Railway transportation is the main means of transportation for people and the main way of logistics transportation, playing an important role in daily life. Therefore, the safety inspection of railway track has been widely valued. The abnormal intelligent detection of rail fasteners is the key content of rail safety detection. The traditional rail fastener detection method is based on machine learning for image recognition, such as SVM, to detect abnormal rail fasteners. But the traditional method has two defects. The first point is that the detection time is long, and the second point is that the detection accuracy is low. To solve this problem, a rail fastener anomaly detection model based on SVM optimized by IFOA algorithm is proposed. Firstly, the image of rail fastener is collected and filtered; Then, edge detection and image segmentation are performed to obtain the image of the target area; Finally, the HOG feature and LBP feature of the image are extracted, and the improved IFOA-SVM is used to recognize and classify the features, so as to achieve intelligent rail fastener anomaly detection. The experimental results show that when the IACO-SVM model is iterated to 254 times, the fitness value tends to be stable, which is 0.24. The detection accuracy of the model reaches 99.82%, which is higher than the traditional models, and can meet the work requirements of rail fastener anomaly detection. The rail fastener anomaly detection model based on SVM can improve the efficiency of rail fastener anomaly detection, and has a positive effect on the normal operation of railway transportation. However, the number of experimental samples used in the study is limited, which may lead to some errors in the experimental results. Therefore, it is necessary to increase the number of samples in subsequent studies.
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