基于神经网络的汽车侧面造型评价方法

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。

Evaluation method of vehicle side modeling based on neural network

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.