Rail Surface State Recognition Based on Attention Network and Cost-sensitive Learning under Imbalanced Data
Accurately identifying rail surface state can provide key evidence for improving the traction/braking performance of trains.Focusing on the problems of differing importance of samples within the same class and decreased accuracy of the majority class when traditional cost-sensitive learning is applied to imbalanced rail surface state recognition,a rail surface state classification method based on attention networks and cost-sensitive learning was proposed.Firstly,transfer learning was utilized to transfer features from a balanced dataset to an imbalanced rail surface state dataset,alleviating the impact of misclassification in the minority class.Secondly,a convolutional block attention module was introduced into the ResNet18 backbone network to enhance the feature learning capability within the target region and the perceptual ability of global feature information,while adjusting and optimizing the network's weight parameters.Finally,an adaptive weighted balanced loss function was constructed based on the importance of rail surface state samples,reducing the overfitting of the decision boundary to the majority class in hard samples and obtaining a smoother decision boundary.Experimental results on imbalanced data demonstrate that the proposed method achieves accuracy and recall of 96％,90.67％,and 86.33％respectively under three different imbalanced ratios.Compared to commonly used methods Focal,the proposed method exhibits improvements of 7％,2.34％,and 3％in terms of both accuracy and recall respectively.Furthermore,the method effectively maintains the recall of the majority class while improving the recall of the minority class,and reduces the training time cost of the network.
rail surface state recognitionimbalanced datacost-sensitive learningattention mechanism
万方数据
维普
