Evaluation Method of Product Emotional Design Based on Computer Vision

DUAN Jinjuan; BAI Zhewen; LUO Pingsheng; SUN Feng'ao; ZHENG Lei

doi:10.19554/j.cnki.1001-3563.2025.14.007

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (14) : 69-75. DOI: 10.19554/j.cnki.1001-3563.2025.14.007

Industrial Design

Evaluation Method of Product Emotional Design Based on Computer Vision

DUAN Jinjuan^1,*, BAI Zhewen², LUO Pingsheng³, SUN Feng'ao⁴, ZHENG Lei⁵

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Abstract

To address the limitations in current product design evaluation methods, such as insufficient consideration of users' emotional preferences, lack of timeliness, and objectivity, the work aims to propose a computer vision-based evaluation method for emotional design from the perspective of Kansei Engineering (KE) by taking forklift product design as an example. Firstly, a product sample library was created with web scraping techniques. Based on KE principles, a perceptual image survey was designed and experimental research was conducted to establish a dataset for evaluating users' perceptual preferences. Next, based on the deep residual network (ResNet), an emotional preference evaluation model, KE-Resnet101, was constructed, converting the design evaluation task into an assessment of emotional preferences for design solutions. Finally, a case study was carried out to rigorously examine and validate the effectiveness of the model, with additional comparative analyses by ResNet50 and ResNet152 models. The KE-Resnet101 model achieved high accuracy and robustness, enabling rapid identification of the product design that aligned most closely with users' emotional preferences, thereby enhancing evaluation efficiency, objectivity, and accuracy.

Key words

Kansei engineering / emotional design / design evaluation / deep learning / computer vision

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DUAN Jinjuan, BAI Zhewen, LUO Pingsheng, SUN Feng'ao, ZHENG Lei. Evaluation Method of Product Emotional Design Based on Computer Vision[J]. Packaging Engineering. 2025, 46(14): 69-75 https://doi.org/10.19554/j.cnki.1001-3563.2025.14.007

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