## 4 Experimental Results
### Dataset and Experimental Setup  
#### 4.1.1 Dataset Configuration  
To improve the generalization ability of the weld seam recognition model in complex environments, we used the HF_data weld seam dataset [38]. This dataset includes various weld seam features and their performance in noisy conditions. As shown in Figure 3, the dataset is designed to simulate the complex scenarios that may occur in weld seam recognition tasks, focusing on issues such as blurred weld seam edges, irregular shapes, and noise interference. First, we selected 192 high-quality weld seam images from the original dataset. Then, we used various data augmentation techniques (such as mirroring, random rotation, resolution adjustment, and adding Gaussian noise) to expand the dataset to 1152 images (each image has a resolution of 512 x 410 pixels). The specific data augmentation process includes horizontal and vertical flipping for mirroring, random rotation within a range of \(\pm\)45\({}^{\circ}\), and adding Gaussian noise with a mean of 0 and a standard deviation of 0.01. This ensures the diversity of the dataset.  
This augmentation method simulates the scenario of capturing weld seams at different angles, enhancing the model's robustness to changes in viewpoint. By incorporating this technique, the model can accurately recognize weld seams from various perspectives, thereby improving its reliability in real-world industrial applications. Additionally, the addition of Gaussian noise helps the model learn to handle different types of interference. The variation in noise conditions simulates the complex environments encountered in real-world scenarios, such as sensor noise and lighting changes. By introducing noise during the training process, DRST-Net becomes better equipped to handle the diverse sources of interference that may be encountered in practical applications.  
Compared to traditional weld seam datasets, we build a dataset that combines different weld seam images with various external noise scenarios, making it more representative of industrial welding environments. The dataset simulates practical challenges such as the edge blurring of weld light stripes, discontinuity in weld textures, and diverse noise interference by introducing noise from different sources. The dataset is randomly divided into training, testing, and validation sets in a 6:2:2 ratio. To ensure accurate annotations, we used the EISeg [39] tool to relabel the weld seam areas. The labeling process combined semi-automatic methods and manual corrections to guarantee accuracy. By developing this weld seam dataset, we have significantly enhanced the model's capability of learning and accurately extracting weld seam features. The dataset also provides robust support for complex weld seam recognition tasks and helps address key challenges in real industrial settings. Specifically, by simulating a variety of complex environments and incorporating different types of noise, the dataset has improved the model's robustness and generalization capabilities under noisy conditions. Consequently, the model can more effectively recognize diverse weld seams in actual industrial environments.