摘要
通过探讨一种基于机器学习的钢轨表面缺陷分类识别算法。该算法首先进行缺陷特征量的计算和提取,随后利用缺陷特征数据集对机器学习算法模型进行训练,最终利用训练好的算法模型对铁轨表面的缺陷进行识别和分类。
An algorithm for classifying and identifying rail surface defects based on machine learning is explored.The algorithm firstly carries out the calculation and extraction of defective feature quantity,and then uses the defective feature dataset to train the machine learning algorithm model,and finally uses the trained algorithm model to identify and classify the defects on the rail surface.
作者
庞少杰
徐燕
李双
Pang Shaojie;Xu Yan;Li Shuang(School of Automotive Engineering,Nantong Polytechnic Institute,Nantong Jiangsu 226300,China)
出处
《机械管理开发》
2024年第10期6-8,共3页
Mechanical Management and Development
基金
南通理工学院2023年江苏省大学生创新创业训练计划项目“轨道探伤小型光伏机器人”(202312056041Y)。
关键词
机器学习
钢轨伤损检测
缺陷分类
machine learning
rail injury detection
defect classification