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Rail State Recognition Method Based on a Convolutional Block Attention Module
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Rail State Recognition Method Based on a Convolutional Block Attention Module
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Journal Article
Rail State Recognition Method Based on a Convolutional Block Attention Module
2024
Overview
In order to solve the problems of high subjectivity and serious lag in traditional rail surface state recognition based on manual experience, a rail surface state recognition model based on convolution attention is proposed. First, the transfer learning method is used to pre-train the ImageNet data set to obtain the model parameters. Secondly, the 3×3 convolution in the ResNet-50 residual block is replaced with a convolutional block attention module (CBAM) to obtain a new CBAM-ResNet. Finally, the model identification results of the rail surface status are obtained through the Softmax classifier. The results show that this method can effectively identify the rail surface status, and the identification accuracy can reach 99.68%, which is better than other deep neural network models.
Publisher
IOP Publishing
