基于SVM的实时自动超声钢轨伤损检测分类系统

介绍了一个更有效的基于支持向量机的实时超声波钢轨伤损自动检测分类系统.根据钢轨伤损的特点提取特征量,利用基于支持向量机的分类预测算法实现钢轨伤损的实时检测分类,并基于统计处理的计算伤损尺寸.在嵌入式系统DSP中利用该机器学习算法实现了伤损的实时处理和测试.实现了钢轨伤损实时报警、显示伤损类型、所处轨内位置及程度.

Automatic real-time SVM-based ultrasonic rail flaw detection and classification system

This paper describes a more efficient real time SVM(support vector machine)-based ultrasonic rail defect detection and classification system. Feature extraction is achieved based on the attribute of ultrasonic rail defect and then SVM classification prediction algorithm and statistical processing are used to realize classification and calculating the size of the rail defect. This machine learning algorithm is tested in DSP and the type, grade and location of the defects are displayed in real-time.