An improved U-Net model is proposed to improve the efficiency of laser stripe extraction to address the issue of decreased accuracy in weld seam feature extraction caused by noise interference such as arc light in actual welding environments. First, VGG16 is used as the encoder foundation and transfer learning strategies are incorporated to enhance the robustness of the model. Second, in the decoder section, a lightweight cross-layer connectivity network module is designed by combining standard convolution and depthwise separable convolution in a multiscale fusion module as well as by introducing a bottleneck layer structure to reduce the computational burden and parameter count of the model. Additionally, this study proposes a lightweight coordinate attention mechanism to enhance the feature representation of bottleneck layers, thereby improving segmentation quality. Finally, different resolution features between the encoder and decoder are fused effectively via skip connections. Experimental results show that this method performs well in welding environments with arc interference, thus effectively improving segmentation accuracy and demonstrating its value in practical applications.