基于改进VGG16网络的混合批量训练交通标志识别

doi:10.16180/j.cnki.issn1007-7820.2021.08.002

Abstract

Abstract:

In view of the problem of low recognition rate and long recognition time of the existing traffic sign recognition methods, based on convolution neural network method, an improved convolutional neural network model based on VGG16 is proposed. The number of convolution layers, convolution kernels and pooling layers of the VGG16 network model are modified to enhance the feature extraction ability and simplicity of the network model. The experimental datasets are enhanced by random rotation, scaling, offset and contrast adjustment, and the network model recognition rate is improved by activation function, mixed batch training and early termination regularization methods. In the experiment of the German traffic sign recognition benchmark, the recognition rate of the improved VGG16 network model is 98.98%, and the recognition time of single traffic sign is only 0.24 ms. Compared with other models, the proposed model has obvious advantages in recognition rate and recognition time.

Key words: convolutional neural network, traffic sign recognition, VGG16, convolutional layer, pooling layer, batch size, feature map, regularization

CLC Number:

TP391.4

LIAO Luming,ZHANG Wei. Batch Mixed Training Traffic Sign Recognition Based on Improved VGG16 Network[J].Electronic Science and Technology, 2021, 34(8): 8-13.

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References 16

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Layer	特征图数目	输出特征图尺寸
Input	1	32×32
C₁	80	30×30
C₂	120	28×28
S₃	120	14×14
C₄	180	12×12
C₅	200	10×10
S₆	200	5×5
C₇	200	3×3
C₈	200	2×2
S₉	200	1×1
Softmax	43	1×1

批量	第1次	第2次	第3次	第4次	平均值
64	98.620	98.670	98.690	98.560	98.635
128	98.400	98.550	98.650	98.520	98.530
256	98.390	98.400	98.470	98.280	98.385
512	98.260	98.320	98.440	98.390	98.352

算法	识别率/%
Random Forests	96.14
文献[13]算法	98.29
Multi-Scale CNNs	98.31
文献[14]算法	98.31
人工分类	98.84
本文算法	98.98
文献[16]算法	99.15
Committee of CNNs	99.46

Batch Mixed Training Traffic Sign Recognition Based on Improved VGG16 Network

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