## 1 Introduction  
As a key process in industrial manufacturing, welding has important applications in the fields of automobile manufacturing, shipbuilding, rail transportation, aerospace, and national defense [1]. With the rapid growth of industrial manufacturing, the complexity of the product has increased. At the same time, there are stricter demands for product quality and production cycles. This has led to greater requirements for welding quality and productivity on industrial production lines [2]. Traditional teach-and-playback welding robots have limitations. Workpiece clamping errors and thermal deformation make it difficult for them to achieve the high precision required in modern industrial welding [3]. Industrial robots can work proficiently in complex and harsh environments. They also prevent workers from operating in dangerous conditions, improving production safety [4]. Compared to manual welding, automated welding offers advantages such as uniform weld seams and stable quality. It has become a major trend in industrial development. In the welding process, various factors can significantly affect the accuracy of weld seam detection, making it a challenging task. During the welding process, factors such as arc instability, metal spatter, and molten pool fluctuations increase the difficulty of finding -held points [5]. As welding requirements continue to increase, the ability of sensors to detect weld positions has become a key technology for improving welding precision and productivity. To date, various sensors, including ultrasonic sensors [5], photodiode sensors [6], magnetic arc sensors [7], and machine vision sensors [8], have been widely applied in weld seam detection systems. Among these, vision sensors have become a research hotspot in automated welding due to their advantages of non-contact detection and high-precision recognition [9]. Among the many vision sensors, laser vision sensors are particularly suitable for weld seam detection in complex environments. This is because they can provide clear weld seam contours and precise location information [10]. In the weld seam recognition process, laser stripes are characteristic patterns projected onto the surface of the workpiece via a laser vision sensor. The formation of these patterns relies on the laser vision system, where a laser projects a laser line onto the workpiece surface at a certain angle, and a camera captures the stripe and images from a vertical angle. Through the scanning and collecting of laser stripe images in the weld seam area, the relevant image data are stored in an industrial control computer. This provides accurate visual information for the subsequent positioning and recognition of the weld seam [11]. In recent years, convolutional neural networks (CNNs) have made significant progress in weld seam feature extraction and recognition. By learning multilevel features, CNNs can accurately extract weld seam features, providing new solutions to the limitations of traditional methods. CNNs automatically learn hierarchical features from raw image data, eliminating the need for manual feature engineering. This not only improves production productivity but also enhances the algorithm's adaptability in complex environments. Many CNN-based models have achieved outstanding results. For example, [PERSON] et al. proposed an adaptive feature extraction algorithm based on laser vision sensors. This algorithm uses the Faster R-CNN model [12] to identify weld seam types and the Steger algorithm to precisely locate the center of laser stripes, accelerating the feature extraction process. However, its robustness in complex environments needs further improvement. [PERSON] et al. introduced a method for structured light and feature point extraction based on the ERFNet network [13]. This method can extract the centerline of welding structured light from noisy backgrounds, but it still faces challenges in integrating multi-scale features and capturing global information. [PERSON] et al.