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图学学报 ›› 2021, Vol. 42 ›› Issue (4): 688-695.DOI: 10.11996/JG.j.2095-302X.2021040688

• 工业设计 • 上一篇    下一篇

基于神经网络的汽车侧面造型评价方法

  

  1. 1. 天津科技大学机械工程学院,天津 300222;
    2. 天津市轻工与食品工程机械装备集成设计与在线监控重点实验室,天津 300222
  • 出版日期:2021-08-31 发布日期:2021-08-05
  • 基金资助:
    国家自然科学基金项目(51505333)

Evaluation method of vehicle side modeling based on neural network

  1. 1. College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China;
    2. Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment,
    Tianjin 300222, China
  • Online:2021-08-31 Published:2021-08-05
  • Supported by:
    National Natural Science Foundation of China (51505333)

摘要: 利用反向传播(BP)神经网络,在 MATLAB 中搭建汽车造型轮廓与意象语义之间的关系模型,快
速判断汽车侧面造型风格。随后利用卷积神经网络(CNN)搭建的表情识别模型建立汽车造型评价系统,分析并
识别用户对于新设计的喜好程度,以得到符合用户情感需求的汽车侧面造型方案。最后通过实例验证方法的可
行性,并推断出流线型汽车的最佳曲率范围。实验结果表明,基于神经网络的汽车造型量化评价方法可以较准
确地对产品造型设计进行评价并以数据形式得到具体意象的侧面造型。

关键词: BP 神经网络, 汽车侧面造型, 意象, 表情识别, 用户情感需求

Abstract: By using the back propagation (BP) neural network, the relationship between car styling contours and
image semantics was constructed in MATLAB to quickly judge the vehicle side modeling style. Then the expression
recognition model built by the convolutional neural network (CNN) was employed to establish the automobile model
evaluation system, and to analyze and identify users’ preferences for the new design, thus obtaining the vehicle side
modeling scheme which can meet users’ emotional needs. Finally, the feasibility of the method was verified through
examples, and the optimal curvature range of the flow-type car was inferred. The experimental results show that the
quantitative evaluation method of automobile modeling based on neural networks can evaluate the product modeling
design more accurately and produce the side shape of concrete image in the form of data.

Key words: BP neural network, vehicle side modeling, imagery, facial expression recognition, user emotional needs

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