摘要
铁轨探伤技术的可靠性关系到铁路运行的安全性。分析BP神经网络、卷积神经网络算法在图片识别中的优势,提出一种结合BP、卷积网络的新算法应用于铁轨伤损检测。改进算法利用卷积神经网络对铁轨样本进行特征提取,仅一次前向运算获得低维度铁轨图,再由BP神经网络对低维度铁轨图特征进行分类训练与测试。实验结果表明,改进算法在已训练好的模型测试中得到较好的误差收敛曲线与较高的测试精度,与BP算法、卷积算法相比,该算法训练时间更少,对铁轨伤损图片识别效果更好,在铁轨伤损检测方面有较好的应用前景。
The reliability of rail flaw detection technology is related to the safety of railway operation.The advantages of BP neural network and convolution neural network(CNN)algorithm in image recognition are analyzed,and a new algorithm combining with BP and convolution network is applied to detect the damage of railway tracks.The CNN is used to extract the feature of the rail sample,and the low dimension rail map is obtained by only one forward operation.Then the BP neural network is used to train and test the characteristics of low-dimensional track.The experimental results show that the improved algorithm obtains better error convergence curve and higher test precision in the trained model test.Compared with BP algorithm and convolution algorithm,the algorithm has less training time and better recognition of rail damage images,so has a good prospect in the field of rail flaw detection.
作者
江白华
张亚
曾文文
JIANG Bai-hua;ZHANG Ya;ZENG Wen-wen(School of Electrical and Information Engineering,Anhui University of Science & Technology,Huainan 232000,China)
出处
《测控技术》
2019年第6期19-22,27,共5页
Measurement & Control Technology
基金
安徽省自然科学基金(1708085QF135)
安徽省高校自然科学基金(KJ2017A077)
安徽理工大学研究生创新基金项目(2017CX2093)
关键词
铁轨探伤
特征提取
卷积神经网络
BP神经网络
rail flaw detection
feature extraction
convolutional neural network
BP neural network
