## 4 Experimental Results
### Quantitative Comparison  
To comprehensively evaluate the performance of the DRST-Net model proposed in this paper, this section compares it with several representative models from recent years. These models include different architectures and technical features, allowing us to assess DRST-Net's advantages from multiple perspectives. The U-Net-based models include U-Net, U-Net++, and nnU-Net. U-Net is a foundational model with a symmetric encoder-decoder architecture, which performs well in simple tasks. However, its performance is often limited in complex backgrounds and noisy environments. To address this, U-Net++ introduces nested skip connections and deep supervision, improving the ability to capture fine-grained features. This leads to better performance, especially for tasks like weld seam recognition with irregular shapes and noisy backgrounds. nnU-Net, on the other hand, automatically optimizes the architecture based on the dataset characteristics, enhancing the model's adaptability across various tasks and demonstrating great flexibility in weld seam recognition. These models were chosen due to their strong performance in medical image segmentation, which shares similarities with weld seam recognition in terms of fine-grained feature extraction and handling irregular shapes. The transformer-based models, such as Swin-Unet and MISSFormer, have a clear advantage in handling complex backgrounds and tasks requiring high detail. Swin-Unet uses a hierarchical Swin Transformer architecture, which accurately models long-rangedependencies. It excels in global information modeling, improving the recognition of weld seam structures. MISSFormer combines hierarchical encoding, enhanced transformer blocks, and context bridging, further improving the performance of capturing weld seam details. These models were selected for their transformer-based architectures, which excel at capturing long-range dependencies and improving robustness in complex backgrounds. Networks like ACC-UNet, BRAU-Net++, DCSAU-Net, and Attention U-Net combine the advantages of CNNs and transformers, achieving good results. ACC-UNet maintains computational performance while modeling long-range dependencies and achieves high precision in weld seam tasks. BRAU-Net++ introduces dual-layer routing attention and redesigned skip connections, strengthening multi-scale feature learning and performing excellently in complex scenes. DCSAU-Net adopts a split attention mechanism, which focuses on feature regions and achieves high accuracy in detail-rich environments. Attention U-Net uses an attention gate mechanism to focus on relevant areas, significantly reducing background interference. These hybrid models combine the strengths of both CNNs and transformers, making them well-suited for balancing computational efficiency and effective feature learning in challenging environments. In the quantitative comparison analysis, we systematically evaluated DRST-Net against the models mentioned above. We used the \(m1 ou\), \(mPre\), \(mRecall\), and F1-_score_ metrics to ensure the comparison was thorough and objective. The experimental results are shown in Table 1. DRST-Net outperforms all the other models on the three evaluation metrics, showing significant advantages. In the key metrics of \(m1 ou\), \(mPre\), and F1_score, DRST-Net achieved 86.17%, 92.32%, and 91.68%, surpassing most of the comparison models, particularly in recognition accuracy and noise adaptability. For example, compared to the classic U-Net and U-Net++, DRST-Net improved \(m1 ou\) and \(mPre\) by 5.69% and 7.22%, respectively. This indicates that DRST-Net has a stronger ability to capture weld seam edge details and local features, improving weld seam recognition accuracy. Compared to transformer-based models such as MISSFormer and Swin-Unet, which both have strong capabilities in global semantic modeling, DRST-Net still performs better in several metrics, especially in detailed feature capture and noise resistance.