## 4 Experimental Results
### Qualitative Comparison  
In industrial weld seam detection, missed detection can lead to missing welding paths, directly affecting weld quality and safety. Based on the comparison in Figure 4, the proposed DRST-Net performs significantly better than other models in detail capture and weld seam accuracy. Models like ACC-Unet, MISFormer, and Swin-Unet perform poorly in predicting missed areas and light strip details, with lower match rates to the true labels. In the third group of data, MISFormer shows clear interference during the recognition process. It fails to thoroughly reduce noise, leading to errors or incomplete identification in some weld seam areas. This suggests that, while the model has a strong global modeling ability, its robustness in noisy environments needs improvement. These models have limitations in handling weld seam edges and complex backgrounds. They fail to capture the fine details of the weld seam, leading to missed detection, especially in complex noisy environments. Problems like missing details and insufficient smoothness at the weld strip turning points are more pronounced. Models such as (d) BRAU-Net and (j) U-Net show discontinuities in weld seam feature capture, while (c) Att-Unet, (e) DCSAU-Net, (h) NUNet, and (k) U-Net++ show stronger noise resistance in complex backgrounds. They handle weld seam boundaries with more detail, reducing misdetection areas. However, they still struggle with capturing smoothness, detail accuracy, and feature changes at weld strip turning points, failing to fully reflect the details of the weld seam edges. This indicates that the existing models still have room for improvement in handling details and adapting to complex environments. In contrast, the proposed DRST-Net, with its dual-branch structure and cross-attention fusion module, significantly improves the ability to capture key point details and suppress noise when processing complex weld seam images. It also enhances the overall weld seam recognition. DRST-Net's dual-branch design allows it to simultaneously process different types of information. One branch captures local details, while the other focuses on global contexts. This separation of tasks enhances the model's ability to capture fine-grained details of the weld seam while maintaining an understanding of the broader structure. This contrasts with models like U-Net, which mainly focuses on local details, and Swin-Unet, which does so globally. By combining these two approaches, DRST-Net can more effectively handle complex and noisy environments, improving both accuracy and noise suppression. CaFF enables DRST-Net to selectively focus on the most relevant features by computing attention across both branches. It improves the model's ability to capture important weld seam details even when faced with noisy or challenging backgrounds. In comparison, models like MISFormer and ACC-Unet may struggle with noise reduction and fine detail recognition because they rely more on standard attention mechanisms or fail to integrate information in a way that fully captures the key features in complex images. ResNet50, through its deep convolutional network, performs multi-level feature extraction from an image, ensuring the accuracy of local information. Swin Transformer, on the other hand, models the complex relationships between these features from a global perspective, allowing the network to better understand the global structure and contextual information of the image. These advantages make DRST-Net more promising for industrial applications, helping to ensure weld quality and precision. The experimental results show that this combination of multi-level feature extraction and global structure modeling leads to a significant performance improvement in DRST-Net for weld seam recognition tasks. Compared to other models that rely solely on local features or global information, such as U-Net or Swin-Unet, DRST-Net can better handle challenges and improve accuracy. Figure 4: Detection results of seam images.