# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 2 Related Work
### Deep Learning Model  
Deep learning models have gained significant attention for their advantages in product design and image aesthetics evaluation, particularly with the advent of the big data era [45; 46; 47; 48; 49; 50; 51]. These models excel in image feature extraction and objective decision-making, often outperforming human-level performance in specific tasks [52; 53; 54; 55]. In the realm of real-time end-to-end detection, frameworks such as DEtection TRansformer (DETR) and the You Only Look Once (YOLO) series are currently among the most popular. While DETR excels in accuracy, its computational complexity poses challenges in balancing precision and speed. Conversely, the YOLO series achieves an optimal trade-off between these two factors, making it highly suitable for end-to-end deployment [56; 57]. Recently, YOLOv5 has found widespread application across various fields, including healthcare [58], industry and transportation [57; 59], and agriculture [60; 61]. Its versatility is particularly evident in industrial applications where both cost-effectiveness and rapid deployment are critical considerations. As illustrated in Table 1, which compares eight detector models on the COCO 2017 validation dataset under identical environmental conditions [62; 63], the YOLOv5s model is particularly notable for its compact size and lightweight architecture. Specifically, Table 1 provides a comparison of parameters (param), floating-point operations (FLOPs), and validation Average Precision (APval) across models. Although YOLOv5s achieves a validation Average Precision of 37.5%, which is lower than some other models, it compensates with significantly fewer parameters and lower computational requirements. Given the moderate complexity of industrial product samples, YOLOv5s' small size and lightweight architecture reduce costs and enable rapid deployment. This makes YOLOv5s an ideal choice for further development in industrial applications. Motivated by these advancements, this study aims to develop an enhanced YOLOv5s deep learning architecture for industrial product form decision-making, particularly to aid designers in understanding user preferences and reducing cognitive biases. The YOLOv5 network is composed of four main components: Input, Backbone, Neck, and Head, as shown in Figure 1. The Backbone processes the input image through a series of Convolution layers (Conv) and C3 layers (containing 3 standard Conv) to extract key features from the image. Finally, a Spatial Pyramid Pooling-Fast (SPPF) module is used to quickly capture multi-scale features, enhancing both the detection and speed of the model. The Neck performs further fusion and up-sampling based on features extracted by the backbone network. It adopts the Path Aggregation Network (PANet) structure, \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Param (M)** & **FLOPs (G)** & **APval (\%)** \\ \hline YOLOv5-S & 7.2 & 16.5 & 37.4 \\ YOLOv6-3.0-S & 18.5 & 45.3 & 44.3 \\ Gold-YOLO-S & 21.5 & 46.0 & 45.4 \\ YOLO-MS-S & 8.1 & 31.2 & 46.2 \\ YOLOv8-S & 11.2 & 28.6 & 44.9 \\ YOLOv9-S & 7.1 & 26.4 & 46.7 \\ YOLOv10-S & 7.2 & 21.6 & 46.8 \\ RT-DETR-R18 & 20.0 & 60.0 & 46.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between detectors on COCO 2017 val in the same environment. which first performs top-down feature fusion followed by bottom-up feature fusion. This structure enhances the detection accuracy and robustness of the model through effective feature fusion and information integration. The Head can fuse and transform features at different scales to capture higher-level semantic information and generate detection results [64, 65]. Compared to traditional machine learning methods, YOLOv5 has superior nonlinear feature extraction and learning capabilities. Its lightweight and efficient model achieves higher accuracy and efficiency in target detection, performing excellently in complex tasks that are challenging to annotate manually [66, 67].