# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 1 Introduction
Although these methods perform well in maintaining consistency for character images, they can result in distortions when applied to real-world photographs. Consequently, these methods are not suitable for real-world automotive product design. Moreover, while maintaining design consistency across vehicle models, research on the consistent segmentation of headlights and grilles remains sparse. To address the need for consistency across vehicle models and improve the generalization of car front face design elements across different models, this paper proposes a segmentation network for vehicle car front face elements. This network accurately separates elements such as headlights and grilles, providing clear structural data for subsequent design processes. Taking headlights and grilles as examples, these components are key styling elements in the front face design of a car. Precise segmentation of these elements provides designers with accurate data for further optimization. In the domains of semantic segmentation tasks, [PERSON] et al. [16] introduced the U-Net model, which uses skip connections linking the encoder and decoder to transfer high-resolution features, significantly improving segmentation accuracy. [PERSON] et al. [17] seamlessly integrated the Transformer architecture with the U-Net framework to form the TransUNet model, addressing U-Net model's lack of global contextual information. This success led to further research on Transformer-based image segmentation models. For instance, [PERSON] et al. [18] developed BGRD-TransUNet for breast lesion segmentation, using DenseNet121 for initial feature extraction, aiding doctors in diagnosis. [PERSON] et al. [19] proposed the EG-TransUNet architecture, significantly improving biomedical image segmentation quality. These models have exhibited outstanding effectiveness in the segmentation of medical images and industrial defect detection. TransUNet combines U-Net's detailed feature extraction and spatial retention with Transformer's global context modeling, making it effective for complex images. Since car front face segmentation requires an understanding of the entire structure and the relationships between components, the Transformer module captures the global position and context of headlights and grilles, while U-Net's skip connections retain high-resolution features for accurate boundary segmentation. Therefore, TransUNet enables precise segmentation of car front face elements like headlights and grilles. Advanced deep learning techniques have significantly enhanced the precision of image semantic segmentation, offering more accurate and efficient tools for automotive design. In automotive segmentation research, [PERSON] et al. [20] proposed an improved YOLOv8 segmentation model (EIS-YOLO) for detecting car surface damage, introducing multi-scale feature fusion and high-resolution feature retention, improving accuracy and computational efficiency. [PERSON] et al. [21] developed an instance segmentation algorithm based on the SPG model, using the GAM attention mechanism to enhance multi-scale feature fusion for classifying parts in automotive scrap dismantling. [PERSON] et al. [22] introduced a monocular vision-based approach for detecting and measuring vehicle outlines, achieving high-precision size estimation. Current research mainly focuses on damage detection, component segmentation, and overall vehicle segmentation. However, when it comes to more complex front face details like headlights and grilles, which possess rich details and intricate shapes, they are more susceptible to noise and other factors, often leading to inaccuracies in segmentation. Therefore, this paper proposes MD-TransUNet, a semantic segmentation network integrating the multi-scale attention gate (MSAG) [23] and the dynamic-channel graph convolutional network (DCGCN) [24], based on the TransUNet model. The term 'MD' is an acronym for the MSAG and DCGCN modules, reflecting their combined role in enhancing the network's performance.