Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.

Kansei评估对于Kansei工程过程至关重要。但是，传统方法是主观的和随机的。为了消除产品感性属性评估中各个评估标准的差异，并进一步提高评估效率，提出了一种基于卷积神经网络的产品感性属性自动评估和标记架构。该体系结构由两个模块组成：（1）目标检测模块（以快速R-CNN为例），（2）细粒度分类模块（以DFL-CNN为例）。提供了一个案例研究来验证所提出的体系结构。所提出的体系结构将设计评估任务转换为识别和分类任务。实验获得了98.837％，96.899％，86.047％和81。二元，三元和两个五分类任务的精度分别为008％。我们的结果证明了使用计算机视觉来模仿人类视觉以自动评估关西属性的可行性。