# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 3 Constructing NTTSSD Paradigms for Imagery Style of Industrial Product Forms
### Quantifying the Imagery Style of Industrial Product Forms Based on Kansei Engineering  
#### 3.1.1 Acquisition of Cross-Category Industrial Product Forms  
In this study, images of 20 commonly seen industrial product forms were collected from major e-commerce platforms, including Amazon, Taobao, and JD.com. The selected product categories covered consumer electronics (e.g., hair dryers, humidifiers, coffee machines, computer mice, loudspeakers), household appliances and furnishings (e.g., lamps, vases, chairs, swived chairs with five-star bases, air fryers, rice cookers, washing machines, ovens, and refrigerators), as well as transportation equipment and other tools (e.g., wheel hubs, cars, helmets, drills, shoes, and medical devices). All collected images were converted into grayscale and underwent background standardization to minimize the influence of color, texture, and environmental noise. To ensure morphological diversity in the dataset, six senior industrial designers with over ten years of professional experience were invited to participate in the sample selection. The expert panel included four industry practitioners (e.g., design firm directors and electronic product supervisors) and two academic researchers (associate professors in industrial design). As shown in Figure 2, the experts selected 80 representative samples from the 20 product categories based on criteria including morphological diversity, market representativeness, and design innovativeness. These selected samples were later used for evaluating user preferences regarding product form. Figure 1: YOLOv5 framework architecture. 3.1.2. Establishing the Vocabulary of Imagery Styles in Industrial Product Forms  
Considering individual differences in cognitive levels, customers may have difficulty accurately expressing their needs. To address this issue, 100 adjectives describing industrial product forms from product websites, magazines, and literature were collected, as shown in Table 2. The globally utilized adjectives were chosen to be free of praise or criticism. Subsequently, six industrial designers with over ten years of professional experience (four industry practitioners and two academic researchers) analyzed the semantic similarity of these adjectives. Several car types (including sports car, intelligent car, off-road car, business car, mini car, and racing car) with the highest global user recognition were selected. These car types were quantitatively ranked and analyzed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and critical imagery style words were identified to provide more precise user support. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Serial** & **Imagery** & **Serial** & **Imagery** & **Serial** & **Imagery** & **Serial** & **Imagery** \\
**Number** & **Style Words** & **Number** & **Style Words** & **Number** & **Style Words** & **Number** & **Style Words** \\ \hline
01 & Tidy & 26 & Delicate & 51 & Austere & 76 & Geometric \\
02 & Unaffected & 27 & Traditional & 52 & Lithe & 77 & Respected \\
03 & Ponderous & 28 & Exaggerated & 53 & Soft & 78 & Ecological \\
04 & Urbane & 29 & Meticulous & 54 & Airy & 79 & Exquisite \\
05 & Graceful & 30 & Comfortable & 55 & Placid & 80 & Lively \\
06 & Flexible & 31 & Directional & 56 & Smooth & 81 & Decorative \\
07 & Revolving & 32 & Interesting & 57 & Masculine & 82 & Individual \\
08 & Rough & 33 & Overbearing & 58 & Concise & 83 & Mysterious \\
09 & Bold & 34 & Advanced & 59 & Trendy & 84 & High-end \\
10 & Unique & 35 & Modern & 60 & Plush & 85 & Newfangled \\ \hline \end{tabular}
\end{table} 
Table 2: One hundred imagery style words for industrial product forms. Figure 2: Eighty sample images of product form. Second, a 5-point Likert scale was used by six experts to score the relevance of the imagery style words within each category.