25 October 2016 A model of traffic signs recognition with convolutional neural network
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101570V (2016) https://doi.org/10.1117/12.2244913
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.
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Haihe Hu, Haihe Hu, Yujian Li, Yujian Li, Ting Zhang, Ting Zhang, Yi Huo, Yi Huo, Wenqing Kuang, Wenqing Kuang, } "A model of traffic signs recognition with convolutional neural network", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101570V (25 October 2016); doi: 10.1117/12.2244913; https://doi.org/10.1117/12.2244913
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