# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 4 Imagery Style Evaluation (ISE) Method for Cross-Category Industrial Product Forms
### Testing the Generalization Ability of the ISE Model on Different Industrial Products
#### 4.3.2 Validation Results and Analysis  
The RMSE calculated according to Equation (20) for the ISE method is 0.26, as shown in Table 8. This indicates that the evaluation model effectively captures and represents consumers' perceptions of product form in terms of visual appearance and design factors. Despite the subjectivity and complexity of morphological aesthetic factors, the deep learning network effectively identifies discriminative key features from visual data. It is worth noting that, as shown in Figure 13, this study presents the evaluation results of the imagery styles of selected product forms through a comparative experiment (Group A: known categories; Group B: unseen categories, including both successful and failed cases). The predicted imagery styles are indicated by colored borders: red (Dynamism), green (Steadiness), orange (Softness), yellow-green (Toughness), yellow (Technology), and pink (Naturalness). Through a systematic qualitative analysis, this study explores the key factors influencing the model's imagery style evaluations, identifying three critical dimensions:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{5}{c}{**AP (\%)**} & \multicolumn{1}{c}{**mAP**} & \multicolumn{1}{c}{**F1**} & \multicolumn{1}{c}{**FPS**} \\ \cline{2-9}  & **Na** & **Te** & **To** & **So** & **St** & **Dy** & **(\%)** & **(\%)** \\ \hline Enhanced YOLOv5 & 91.8 & 85.4 & 76.3 & 87.1 & 86.3 & 89.8 & 86.1 & 82 & 106.4 \\ YOLOv5 & 89.9 & 76.7 & 66.2 & 86.2 & 85.3 & 83 & 81.2 & 76 & 104.2 \\ YOLOv10 & 90.1 & 83.7 & 73.4 & 84.2 & 87.9 & 88.8 & 84.7 & 80 & 147.1 \\ SVM & 54 & 57 & 32 & 43 & 63 & 61 & 52 & 66 & 195.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison experimental results.  
1. Coupling the effect of feature distinguishability and background interference: When the product has a simple structure and highly distinguishable features (e.g., a trash bin with sharp lines and a rigid form, or headphones with smooth, rounded edges), the model can accurately evaluate the imagery style regardless of background complexity. In contrast, when the structure is complex and features are ambiguous (e.g., cases Bf1, Bf2, Bf3), the model tends to misclassify the imagery style even against a simple background, due to overlapping or conflicting stylistic cues.
2. Structure-feature balance under background-free conditions: For complex products with homogeneous and dominant features (e.g., bicycles, robots), the model can effectively extract and evaluate key stylistic traits. However, for products with simple structures but conflicting features (e.g., cases Bf4 and Bf5), the model struggles due to semantic interference among competing stylistic signals, leading to ambiguous or incorrect predictions.
3. Feature competition in multi-object scenes: Products with complex structures and salient features (e.g., electric shavers, projectors) can still be correctly evaluated even with simple or cluttered backgrounds. However, in scenes containing multiple objects--even if each object has a simple and clear structure (e.g., case Bf6)--feature overlap and competition between objects introduce significant noise, often resulting in inconsistent or inaccurate assessments.  
In summary, the combination of product structural complexity, the distribution of salient features, background complexity, and scene composition all significantly influence the accuracy of the model's imagery style evaluations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clusters** & **Total Number** & **Number of Predicted Successes** & **mAP (\%)** & **RMSE** \\ \hline A & 860 & 802 & 86.1 & \\ B & 1077 & 1005 & 80 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results from two sets of experiments using the ISE method.  
Figure 13: Examples of evaluation results from two experimental sets.