Abstract: To solve the problem of poor category prediction effect of direct integration of simple classifiers to recognize traffic sign databases, a traffic sign recognition algorithm based on Convolutional Neural Network and Bagging ensemble learning is proposed, using crawler and image enhancement technology to expand the data set of traffic signs, and the features of traffic sign images is extracted by Convolutional Neural Network. For the characteristics of the logo image, the image data are down-sampled by using the maximum pooling layer, and the overall network structure is simplified by using a shallower network depth. On the basis of Convolutional Neural Network feature extraction, the soft voting mechanism is used to integrate Multiple Logistic, K nearest Neighbor, and SVM individual learners to obtain a better prediction result and achieve more accurate traffic sign recognition. Experimental results show that the recognition accuracy of the algorithm on the TSRD traffic sign recognition database test set has reached 93.00%, which is 11.99% higher than the recognition accuracy of the original Network model, and is higher than the recognition accuracy achieved by VGG16 and ResNet50 migration learning having a faster convergence speed.

Key words: image enhancement, convolutional neural network, transfer learning, integrated learning, traffic sign recognition