基于机车中性截面图像HoG特征提取的机器学习分类器

Christopher Thembinkosi Mcineka, N. Pillay
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摘要

本文对中性切片图像分类的机器学习算法进行了比较研究。分类器通过使用从中性截面数据集中提取的定向梯度特征直方图进行训练[1]。中性段是在Transnet货运铁路系统中用于将单相电源与25kV三相架空牵引电源分开的断相段。25kV是来自国家电网的8SkV三相电源的降压电压。中性部分的主要目的是分离相电压,电力机车可以通过开断开关来穿越这些相。这种自动切换可以通过安装在轨道之间的感应磁铁和安装在机车下面的磁铁检测传感器来实现。然而,一个计算机视觉模型已经被开发、训练和测试,使用一个包含具有打开和关闭标记的图像的中性截面数据集[1]。因此,本文利用该数据集提供几种机器学习分类算法的性能比较,即决策树、判别分析、支持向量机、k近邻、集成、Naïve贝叶斯和卷积神经网络。使用混淆矩阵、f1测度和计算时间来衡量每个分类器的性能。使用MATLAB分类学习器应用程序获得结果。结果表明,在性能和预测速度两方面,线性支持向量机的预测效果最好。线性支持向量机的训练准确率达到93.40%,测试准确率达到94%,预测速度为每秒75个对象(计算时间)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral Section Images
This paper presents a comparative study on machine learning algorithms for neutral section image classification. The classifiers are trained by employing the Histogram of Oriented Gradient features that are extracted from the neutral section dataset [1]. A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 8SkV three-phase supply coming from the national grid. While the main purpose of the neutral section is to separate phase voltages, electric locomotives can traverse through these phases by switching On and Off. This auto-switching is possible through induction magnets installed in between the rails and with magnet detection sensors installed underneath the locomotives. However, a computer vision model has been developed, trained, and tested with a neutral section dataset containing images having open and close markers [1]. This paper, therefore, utilises this dataset to provide performance comparison on several machine learning classification algorithms viz. Decision Tree, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Ensemble, Naïve Bayes, and Convolutional Neural Network. A confusion matrix, F1-measure and computation time are employed to measure the performance of each classifier. The MATLAB Classification Learner application was used to obtain the results. The results show that the Linear Support Vector Machine performs best when considering performance and prediction speed. The Linear Support Vector Machine achieved a training accuracy of 93.40% with a test accuracy reaching 94% at a prediction speed of 75 objects per second (computation time).
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