DCGAN在汽车造型设计模型中的应用

裴卉宁; 谭昭芸; 张金勇; 赵芳华; 黄雪芹

doi:10.13433/j.cnki.1003-8728.20200471

DCGAN在汽车造型设计模型中的应用

doi: 10.13433/j.cnki.1003-8728.20200471

河北工业大学建筑与艺术设计学院, 天津 300401

基金项目:

教育部人文社会科学基金项目 21YJCZH113

河北省高等学校科学研究项目 SD201091

详细信息

作者简介:
裴卉宁(1986-), 讲师, 博士, 研究方向为人因可靠性、计算机辅助工业设计研究, peihuining@hebut.edu.cn

通讯作者:
张金勇, 讲师, 博士, zhangjinyong@hebut.edu.cn

中图分类号: TH166;TB472
计量
- 文章访问数: 122
- HTML全文浏览量: 66
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2020-10-01
- 刊出日期: 2022-10-25

Application of DCGAN to Design Model of Automobile Modeling

School of Architecture and Art Design, Hebei University of Technology, Tianjin 300401, China

摘要

摘要: 为了进一步提升设计方案的效率，缓解人工劳动的强度和压力，通过计算机自动设计达到快速生成创新造型设计方案的目的，提出一种基于深度卷积生成对抗网络(Deep convolution generative adversarial networks，DCGAN)的汽车造型设计模型。该模型通过构建基础产品造型模块的设计方案数据集，利用深度学习的DCGAN对数据集进行训练以提高设计方案图像质量。最后，将生成的两种不同种类汽车造型方案与专家方案进行满意度对比，结果显示模型生成的方案能够得到与专家设计方案相近的评分，证明了所提模型的有效性和合理性。
- 造型设计 /
- 产品设计 /
- 深度卷积生成对抗网络 /
- 汽车设计
Abstract: In order to further improve the efficiency of design schemes and reduce human labor, through the automatic design of computers to achieve the purpose of quickly generating innovative styling design schemes, design model for automobile modeling with deep convolution generative adversarial networks (DCGAN) was proposed. The model builds the design solution data set of the basic product modeling module, and the design plan image quality was improved by using the deep learning DCGAN to train the data set. Finally, comparing the generated two different types of car styling schemes with the expert schemes for satisfaction, the results show that the scheme generated via the present model can get a score similar to the expert design scheme, the validity and rationality of the present model were proved.
- modeling design /
- product design /
- deep convolution generated adversarial network /
- vehicle design

HTML全文

图 1 汽车功能结构模块划分

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图 2 产品造型设计系统框架

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图 3 斯坦福汽车样本

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图 4 网络爬虫汽车样本

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图 5 汽车模块分割举例

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图 6 生成模型结构图

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图 7 判别模型网络结构图

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图 8 不同实验总迭代次数

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图 9 家用汽车不同迭代次数的输出结果

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图 10 SUV不同迭代次数的输出结果

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图 11 专家设计方案

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图 12 家用汽车车型造型设计综合满意率对比

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图 13 SUV车型造型设计综合满意率对比

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图 14 不同模型结果对比

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表 1 One-hot编码结果

家用汽车车型	SUV车型
01	10

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表 2 训练注意细节

步骤	细节
1	实验中进行的预处理只是将训练图像缩放到tanh激活函数的范围[-1, 1]内
2	最小批训练, 批大小是16
3	所有权值都是从以0为中心的正态分布初始化的, 标准差为0.02
4	在LeakyReLU中, 所有模型的斜率都设置为0.2
5	DCGAN使用Adam优化器来加速训练
6	Adam优化器的学习率设置为0.000 2
7	将动量参数β₁从0.9降为0.5以防止震荡和不稳定

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表 3 数据集组成数量

名称	家用汽车车型数量	SUV车型数量
原始数据集	8 565	2 771
网络爬虫方法	1 090	1 531
传统方法	4 360	11 084
总计	14 015	15 386
训练集	11 212	12 308
测试集	2 803	3 077

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