Latest ArticlesTo solve the problem of low accuracy when classifying traffic signs using Convolutional Neural Networks(CNN), this article proposes a cascade super-resolution network structure by connecting an image super-resolution network to a classification network. A modified dual-attention mechanism super-resolution network is first used as a sub-network of the cascade network, and then the image classification network is trained for classifying the super-resolution processed images, and finally the classification accuracy is used to measure the effectiveness of super-resolution reconstruction for the image classification task. The validation results of both simulation and real traffic sign datasets show that the super-resolution processed images achieve higher classification accuracy in the classification model, which proves that the super-resolution technology has a facilitating effect on the improvement of the classification accuracy of traffic sign images.
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