# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 5 Conclusions  
This paper introduces a MD-TransUNet network, which enhances the TransUNet network with the DCGCN and MSAG modules for segmenting headlights and grilles in automotive front face images. The MSAG module is integrated with skip connections to enhance valuable features, while the DCGCN module bridges high-level and low-level features between the encoder and decoder, dynamically adjusting convolution kernels to capture image details and improve segmentation accuracy. The experimental results indicate that the proposed network surpasses six other models, such as DeeplabV3+, DNLNet, and SegFormer, demonstrating outstanding performance on the dataset. Furthermore, by inputting the precisely segmented car front face image masks from the MD-TransUNet model into a diffusion model, diverse car front face designs can be generated. This approach allows for the flexible adjustment of detailed elements, such as headlights and grilles, while maintaining the overall design consistency of the car front face. This aids in analyzing and comparing the visual effects of different design elements, significantly reducing design iteration time and costs, and improving design efficiency. This approach offers new inspiration and direction for the automotive design industry. For example, various shapes and sizes of headlights and grilles layouts can be quickly generated and compared to find the optimal design. Additionally, the MD-TransUNet model's ability to accurately segment fine details of car front elements can be extended to autonomous vehicle systems. By refining the segmentation of complex structures, this model could be adapted for detecting critical road elements like pedestrians, vehicles, and traffic signs. The dynamic convolution kernels in the DCGCN module, which adjust based on feature complexity, offer significant advantages in processing diverse road environments, enhancing object detection and classification accuracy. This contributes directly to improving autonomous driving perception systems, enabling more reliable decision-making in real-time traffic scenarios. Looking ahead, with continuous technological advancements and richer data accumulation, diversified design based on precise image segmentation and generation models will bring more possibilities and breakthroughs to the automotive industry and other industrial design fields. This study is limited by the lack of diverse environmental variables. The dataset used consists solely of frontal car views at 1024 \(\times\) 768 resolution, captured under controlled conditions. However, real-world variations in resolution, lighting, and camera angles are common. Current augmentations like random rotation and flipping provide some robustness but may not address real-world challenges. We propose advanced augmentations, such as coordinate transformations, image warping, perspective distortion, and synthetic lighting variations, to better handle viewpoint changes and improve generalization. Future work will explore these methods to enhance model robustness in diverse conditions. While MD-TransUNet outperforms comparison models across metrics, the visual results show some inconsistencies with annotated images (e.g., Figure 3(b) vs. Figure 3(i), Figure 4(b) vs. Figure 4(f)). Possible reasons include:  
1. Model limitations: Although MSAG and DCGCN enhance global information capture, MD-TransUNet may struggle with fine-grained features like complex edges, leading to less precise segmentation in certain areas;
2. Dataset limitations: The dataset, consisting solely of controlled frontal car views, lacks diversity, limiting the model's ability to generalize to more complex scenarios. Underrepresented features may lead to inaccuracies during testing;
3. Loss function limitations: The pixel-based loss function (e.g., cross-entropy loss) focuses on overall classification but is less sensitive to local details, like edge precision, resulting in segmentation discrepancies. **Author Contributions:** Conceptualization, J.O. and H.S. ; methodology, J.O. ; software, H.S.