Title: Traffic sign recognition based on improved convolutional networks
Authors: Ke Zhang; Jie Hou; Mengyu Liu; Jiayan Liu
Addresses: Shanghai Institute of Technology, Fengxian District, Shanghai, China ' Shanghai Institute of Technology, Fengxian District, Shanghai, China ' Shanghai Institute of Technology, Fengxian District, Shanghai, China ' Shanghai Institute of Technology, Fengxian District, Shanghai, China
Abstract: Real-time and accurate traffic sign detection and identification is a huge challenge under real vehicle driving conditions due to background diversity, illumination intensity, shooting position, lens pixel value and other factors. In this paper, an improved convolutional network based on LeNet-5 is proposed for traffic sign recognition. The inception module is introduced to enhance the performance of feature extraction. The size of convolution kernels is changed to 3×3 and 1×1. In addition, a method of image standardised pre-processing is introduced for batch processing of samples in order to improve the generalisation performance of recognition. Furthermore, the dropout layer is utilised to prevent overfitting. The experimental results show that the improved neural network has good robustness and the network recognition accuracy reaches more than 99%. Compared with the traditional Lenet-5 model, the method has more outstanding performance in the identification of multiple classification problems and has certain advancement for traffic sign recognition.
Keywords: traffic sign; convolutional neural network; image processing; feature extraction; LeNet-5.
International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.3, pp.274 - 284
Received: 26 Jul 2021
Accepted: 21 Dec 2021
Published online: 16 Feb 2022 *