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首页> 外文会议>IEEE International Conference on Electronic Measurement and Instruments >Traffic sign classification network using inception module
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Traffic sign classification network using inception module

机译：使用起始模块的交通标志分类网络

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With the rapid development of the automobile industry, the demand for autonomous driving becomes more and more urgent, and the traffic sign recognition technology in autonomous driving is an indispensable technology. This paper proposes a GoogLeNet based convolutional neural network for traffic signs. This convolutional neural network improves each of the underlying Inception Modules and adds the Batch Normalization layer, effectively avoiding over-fitting of the network. We use a sparse structure that conforms to the Hebbain principle to reduce the parameters and improve the generalization ability of the network, which can extract the features of the image more accurately. Meanwhile, the network also reduces the parameters of the full connection layer by 20 times through the continuous two-layer pooling layer, which greatly speeds up the network training. Finally, the network is trained using the GTSRB data set and the classification accuracy rate can reach 98%. At the same time, we also verified the validity of the network on the MNIST dataset and the pneumonia dataset. The classification accuracy rate can reach 100% on the above two datasets. Experimental results on the above data sets show the validity of the convolutional neural network.
机译：随着汽车工业的快速发展，对自动驾驶的需求越来越迫切，自动驾驶中的交通标志识别技术是必不可少的技术。本文提出了一种用于交通标志的基于GoogLeNet的卷积神经网络。该卷积神经网络改进了每个基础的Inception模块，并添加了Batch Normalization层，有效地避免了网络的过度拟合。我们使用符合Hebbain原理的稀疏结构来减少参数并提高网络的泛化能力，从而可以更准确地提取图像的特征。同时，网络还通过连续的两层池化层将完整连接层的参数减少了20倍，极大地加快了网络训练的速度。最后，使用GTSRB数据集对网络进行训练，分类准确率可以达到98％。同时，我们还在MNIST数据集和肺炎数据集上验证了网络的有效性。在以上两个数据集上的分类准确率可以达到100％。在以上数据集上的实验结果证明了卷积神经网络的有效性。

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