Abstract
In order to improve the accuracy of railway fastener image recognition and training efficiency in the process of machine learning. Two traditional feature extraction methods, MB-LBP features and PHOG features are fused to make up a new image features for training in this paper. At the same time, to solve the problem of low training speed caused by increased feature dimension, introducing Adaboost-SVM classifier, the classification efficiency of Adaboost classifier will be reduced after a certain set of conditions, then the remaining samples are sent to the SVM classifier for classification. The results show that the accuracy of the fusion image feature is 3.9% and 5.8% higher than that of the individual MB-LBP and PHOG, respectively, and the training speed is also significantly improved by using the Adaboost-SVM classifier.
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