基于改进卷积神经网络的交通标志识别方法

针对传统卷积神经网络时间成本高的不足，对卷积神经网络进行了改进，减少其卷积核的数量，增加池化方式.为解决真实场景中自动驾驶系统和辅助驾驶系统中的道路交通标志识别问题，将改进的卷积神经网络运用到道路交通标志识别当中，以达到在较短时间内识别出交通标志的目的.以图形数据集GTRSB实景交通标志图像数据作为样本，用改进的卷积神经网络对实景交通标志进行识别，其识别总体准确率达到98.38%.实验结果表明，本方法可以在保持较高识别准确率的同时减少其识别的时间.

Traffic Sign Recognition Based on Improved Convolutional Neural Network

In view of the high time cost of traditional convolutional neural network, an improved convolutional neural network is designed, which has a reduction in the number of the convolutional kernels and an increase of the pooling methods. To solve the road traffic sign recognition problem of autopilot system and auxiliary driving system in the real scenario, the improved convolutional neural network is applied to road traffic sign recognition for the purpose of identifying traffic sign in a relatively short period of time. Taking the graphic data set GTRSB, real traffic sign image data as a sample, the real traffic sign is identified, and the overall recognition accuracy reaches 98.38%. Experimental results show that this method can reduce the recognition time while maintaining high recognition accuracy.