## 1 Introduction
developed a multi-feature combination network based on the SSD framework, tailored to the characteristics of weld seam detection tasks. By combining detailed and comprehensive information from multiple feature layers, the network enables fast weld seam detection. However, there is room for improvement in feature fusion efficiency and the preservation of fine details [14]. Although these studies have provided valuable solutions for weld seam recognition, they still face significant challenges, including noise interference, local information redundancy, and limited adaptability to unstructured scenes. These critical limitations significantly hinder the models' ability to further improve their accuracy, restricting their effectiveness in real-world industrial applications. The existing methods for weld seam recognition, particularly traditional image processing techniques such as edge detection, threshold segmentation, and morphological processing, face significant limitations when applied in complex environments. These methods are primarily effective in controlled conditions with minimal interference. However, in real-world industrial scenarios, the challenges posed by factors such as arc instability, metal spatter, smoke, and varying lighting conditions significantly degrade their performance. In industrial weld seam detection, accurately identifying weld seams in complex, noisy environments is crucial for ensuring weld quality and safety. Traditional methods often struggle to capture fine details, particularly in areas with high interference or complex backgrounds. The missed detection of welding paths or details can directly impact the quality and integrity of the weld, potentially leading to safety hazards. This study aimed to address these challenges by proposing a more robust and accurate model for weld seam detection in such environments. By tackling these key challenges--particularly in complex, noisy environments--DRST-Net offers a promising solution for industrial applications, ensuring better weld quality and precision. This research contributes to advancing weld seam detection technology, addressing a critical need in industries where welding precision is paramount. To overcome these challenges, we propose DRST-Net, a dual-branch weld seam recognition network with a cross-attention mechanism. This model integrates the ResNet50 and Swin Transformer branches to effectively combine local details and global semantic information. Specifically, the ResNet50 branch focuses on extracting local features, enabling the precise recognition of weld seam edges and subtle variations in the features. This facilitates the accurate detection of fine boundaries and detailed backgrounds. Meanwhile, the Swin Transformer branch enhances the model's ability to capture long-range dependencies, extracting global semantic information such as the overall shape of the weld seam, its spatial arrangement, and its interaction with background noise. By integrating these two branches, DRST-Net improves the model's robustness and recognition accuracy in complex environments. With its dual-branch structure, the model achieves precise recognition of fine details while maintaining a comprehensive understanding of global information, making it better suited for the variability and complexity of real-world welding images. Additionally, we introduce the Cross-Attention Feature Fusion (CaFF) module to successfully merge local features and global semantic information. This module performs cross-attention fusion on features extracted from the ResNet50 and Swin Transformer branches, enabling multi-level information interaction and strengthening the correlation between detailed and comprehensive features. This results in a model that is more effective in complex environments and better suited for the challenges faced in industrial welding applications. The main contributions of this paper are as follows:  
1. We propose a dual-branch network called DRST-Net, which combines ResNet50 and Swin Transformer as encoders.