Traffic Sign Recognition Based on DF-Stacking Model
To address the problems that traditional machine learning methods have poor noise immunity and deep learning meth-ods rely on a large number of training samples,this paper proposes a traffic sign recognition method based on deep forest-Stac-king(DF-Stacking).Multi-grained scanning combined with cascade forest is used to extract image features.The obtained fea-tures are input to the Stacking ensemble module to classify images.The results show that the DF-Stacking model has signifi-cantly improved feature extraction accuracy over the deep learning method when using a small number of training samples.In addition,the classification accuracy of the proposed DF-Stacking model is higher than that of the single classifier method under the conditions of Gaussian noise and motion blur,reflecting the strong generalization ability.
万方数据
维普
