现有交通标志识别技术, 存在高识别率高功耗或者低识别率低功耗的问题, 构建了新轻量级WACNN的识别算法。首先, 利用TensorFlow构建6层卷积神经网络, 其中前三层为卷积池化层, 四层为1×1卷积层, 五层为全连接层, 六层为输出层, 前四层再加入批量归一化方法。其次, 使用直方图均衡对交通图像预处理。最后, 模型在GTSRB上进行实验, 实验结果表明, 所提模型不仅极大缩短了训练时间, 且识别准确率也能达到了97％。

The existing traffic sign recognition technology has the problems of high recognition rate, high power consumption or low recognition rate and low power consumption. Aiming at this problem, a new light WACNN of recognition algorithm is constructed. Firstly, six layers convolutional neural network are constructed by using TensorFlow, in which the first three layers are convolutional pooling layers, the fourth layer is 1×1 convolutional layer, the fifth layer is fully connected layer, the sixth layer is output layer, and the first four layers are then added batch normalization method. Secondly, histogram equalization is used to preprocess traffic sign images. Finally, the model is tested on GTSR. The experimental results show that the proposed model not only greatly shortens the training time, but also the recognition accuracy can reach 97%.