# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
###### Abstract  
The evaluation of imagery style in industrial product design is inherently subjective, making it difficult for designers to accurately capture user preferences. This ambiguity often results in suboptimal market positioning and design decisions. Existing methods, primarily limited to single product categories, rely on labor-intensive user surveys and computationally expensive data processing techniques, thus failing to support cross-category collaboration. To address this, we propose an Imagery Style Evaluation (ISE) method that enables rapid, objective, and intelligent assessment of imagery styles across diverse industrial product forms, assisting designers in better capturing user preferences. By combining Kansei Engineering (KE) theory with four key visual morphological features--contour lines, edge transition angles, visual directions and visual curvature--we define six representative style paradigms: Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism (NTTSSD), enabling quantification of the mapping between product features and user preferences. A deep learning-based ISE architecture was constructed by integrating the NTTSSD paradigms into an enhanced YOLOv5 network with a Convolutional Block Attention Module (CBAM) and semantic feature fusion, enabling effective learning of morphological style features. Experimental results show the method improves mean average precision (mAP) by 1.4% over state-of-the-art baselines across 20 product categories. Validation on 40 product types confirms strong cross-category generalization with a root mean square error (RMSE) of 0.26. Visualization through feature maps and Gradient-weighted Class Activation Mapping (Grad-CAM) further verifies the accuracy and interpretability of the ISE model. This research provides a robust framework for cross-category industrial product style evaluation, enhancing design efficiency and shortening development cycles.  
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of user emotional feedback data is conducted, a better morphological design artifact can be created during the product conceptual design stage when the data reflect user preferences.  
To better understand users' emotional needs, some scholars have utilized Eye Tracking and Electroencephalography (EEG) techniques [5; 6; 7; 8; 9] to precisely measure consumer attention to specific regions of products. Additionally, surveys and interviews have been conducted to explore user emotional preferences, aiding in guiding specific product designs [10; 11]. However, these traditional methods face some limitations: they are often labor-intensive, costly, time-consuming, and limited in scale. Compared to traditional methods, applying machine learning techniques to analyze user emotional preferences for product forms is both low-cost and efficient [12]. Therefore, some scholars use machine learning methods to predict user preferences by analyzing small sample data of certain product forms and local design element parameters. In particular, certain of these algorithms have become mainstream applications in the field of product form, such as Support Vector Machines (SVM) [13; 14], Random Forest [15], Bayesian networks [16; 17; 18], Decision Trees [19; 20], and artificial neural networks [21; 22; 23; 24; 25]. While machine learning methods are more efficient than traditional approaches, they are primarily used to predict user preferences for specific product forms in small samples. Additionally, for machine learning to be applied to design problems, design variables must be numerical or convertible to numerical data [26]. After multiple rounds of data interpretation and complex data conversions, the objectivity of the information may significantly decrease. Furthermore, it becomes challenging for general designers to deal with large volumes of data in various formats.