基于恒虚警率的深度神经网络Dropout正则化方法

肖家麟; 李钰; 袁晴龙; 唐志祺

doi:10.14135/j.cnki.1006-3080.20201127005

基于恒虚警率的深度神经网络Dropout正则化方法

doi: 10.14135/j.cnki.1006-3080.20201127005

华东理工大学信息科学与工程学院，上海 200237

详细信息

作者简介:
肖家麟（1995-），男，湖南长沙人，硕士生，主要研究方向为智能传感与信息处理、物体识别。E-mail：y30180629@mail.ecust.edu.cn

通讯作者:
李　钰，E-mail：liyu@ecust.edu.cn

中图分类号: TP391
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- 文章访问数: 526
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-27
- 网络出版日期: 2021-03-24
- 刊出日期: 2022-02-23

Dropout Regularization Method of Convolutional Neural Network Based on Constant False Alarm Rate

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

摘要

摘要: 为进一步提高深度神经网络算法在嵌入式机器人系统中的物体识别性能，提出了一种基于恒虚警率检测的深度神经网络Dropout正则化方法（CFAR-Dropout）。首先，通过对权重进行量化，将权重和激活从浮点数减少到二进制值；然后，设计了一个恒虚警检测器（CFAR），保持一定的虚警率，自适应地删减一些神经元节点，优化参与计算的神经元节点；最后，在嵌入式平台PYNQ-Z2上，使用基于VGG16的优化模型对算法的物体识别性能进行实验验证。实验结果表明，与使用经典的Dropout正则化方法相比，CFAR-Dropout正则化方法的错误率降低了约2%，有效防止了过拟合；与原本的网络结构相比，参数量所占内存减少到8%左右，有效防止了过参数化。
- 物体识别 /
- 嵌入式 /
- 深度神经网络 /
- 恒虚警率 /
- 正则化
Abstract: Compared with the traditional machine learning algorithms, deploying deep neural networks in embedded systems can significantly improve the performance of robot system object recognition. However, its performance is limited by the computing resources and memory capacity of the embedded platform. Hence, it is quite necessary to simplify the network structure and improve the system efficiency through model pruning and parameter quantification. Meanwhile, it is also necessary to prevent overfitting through dropout regularization and improve the accuracy of system recognition. In order to further improve the object recognition performance of the deep neural network algorithm in the embedded robot system, this paper proposes a deep neural network dropout regularization method based on constant false alarm detection (CFAR-Dropout). Firstly, by quantizing the weights, the weights and activations are reduced from floating point numbers to binary values. Secondly, a constant false alarm detector (CFAR) is designed to maintain a certain false alarm rate, adaptively delete some neuron nodes, and optimize the neuron nodes involved in the calculation. Finally, on the embedded platform PYNQ-Z2, an VGG16-based optimization model is used to experimentally verify the object recognition performance of the proposed algorithm. It is shown via experimental results that compared with the classic dropout regularization method, the CFAR-Dropout regularization method can reduce the error rate by about 2%, and effectively prevent the overfitting. Moreover, compared with the original network structure, the proposed algorithm can reduce the amount of the occupied memory by about 8%, and effectively prevent over-parameterization.
- object recognition /
- embedded /
- deep neural network /
- constant false alarm rate /
- regularization

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图 1 Dropout正则化前后对比

Figure 1. Comparison before and after Dropout

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图 2 权重初始值是标准差为1（a）和0.01（b）的高斯分布时激活值的分布

Figure 2. Initial weight value is the distribution with the activation value with Gaussian distribution of standard deviations 1（a） and 0.01（b）

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图 3 权重初始值是标准差为$ { 1/\sqrt{n}} $的高斯分布时激活值的分布

Figure 3. Initial value of the weight is the distribution of the activation value with a Gaussian distribution of standard deviation $ { 1/\sqrt{n} }$

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图 4 CFAR-Dropout原理简图

Figure 4. CFAR-Dropout schematic diagram

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图 5 卷积和池化部分IP核

Figure 5. Binarization error rate convolution and pooling of IP cores

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图 6 物体识别系统检测步骤

Figure 6. Object recognition system detection steps

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图 7 恒虚警率不同时的测试错误率

Figure 7. Test error rates with different constant false alarm rates

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图 8 实验设备

Figure 8. Experimental equipment

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图 9 一般物体边缘提取

Figure 9. General object edge extraction

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图 10 细胞培养板边缘提取

Figure 10. Cell culture plate edge extraction

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图 11 细胞培养板的识别结果

Figure 11. Recognition results of cell culture plate

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表 1 网络结构参数表

Table 1. Network structure

Type	Channel	Kernel
CONV1	64	3×3
CONV2	64	3×3
MAXPOOL1	64	2×2
CONV3	128	3×3
CONV4	128	3×3
MAXPOOL2	128	2×2
CONV5	256	3×3
CONV6	256	3×3
CONV7	256	3×3
MAXPOOL3	256	2×2
CONV8	512	3×3
CONV9	512	3×3
CONV10	512	3×3
MAXPOOL4	512	2×2
CONV11	512	3×3
CONV12	512	3×3
CONV13	512	3×3
MAXPOOL5	512	2×2
FC1（Dropout）	/	/
FC2（Dropout）	/	/
FC3	/	/

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表 2 不同正则化方法的错误率

Table 2. Error rates of different regularization methods

Model	Method	Training error rate/%	Testing error rate /%
A- MNIST	/	1.56	16.32
A- MNIST	Dropout	2.95	3.12
A- MNIST	CFAR-Dropout	1.89	2.01
A-CIFAR10	/	12.16	22.04
A-CIFAR10	Dropout	14.23	15.26
A-CIFAR10	CFAR-Dropout	12.21	13.51
A-SVHN	/	4.46	18.53
A-SVHN	Dropout	7.38	7.44
A- SVHN	CFAR-Dropout	5.37	5.58

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表 3 不同的正则化方法在PYNQ上的错误率

Table 3. Error rates of different regularization methods on PYNQ

Model	Method	Error rate (LAN)/%	Error rate (Cam)/%
A- MNIST	Dropout	2.98	3.32
A- MNIST	CFAR-Dropout	1.93	2.78
A-CIFAR10	Dropout	14.56	15.04
A-CIFAR10	CFAR-Dropout	12.42	13.05
A-SVHN	Dropout	7.62	9.68
A- SVHN	CFAR-Dropout	5.59	6.05

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表 4 网络层数对正确率的影响

Table 4. Influence of network layer numbers on accuracy

Network layer number	Binarization error rate/%	Floating point error rate/%	Energy efficiency ratio
64	6.43	3.78	2.67
128	6.11	2.73	3.35
512	4.11	1.72	3.54
1 024	2.23	1.21	4.23
2 048	1.37	0.98	6.34

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表 5 不同网络结构的参数量和运行速率

Table 5. Numbers of parameters and running rates of different network structures

Type	Parameters/MB	Convolution layer	Connection layer	GFLOPS
VGG16	138.36	13	3	15.5
MobileNet	4.2	5	3	0.54
ResNet50	25.6	53	1	3.9
A	10.5	13	3	241.3

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