CGAN based anti-interference recognition method for weld seam images

Common strong noise interferences like metal splashes, smoke, and arc light during welding can seriously pollute the laser stripe images, causing the tracking model to drift and leading to tracking failure. At present, there are already many mature methods for identifying and extracting feature points of linear laser stripes. When the laser stripe forms a curved shape on the surface of the workpiece, these linear methods will no longer be applicable. To eliminate interference sources enhance the robustness of the weld tracking model, and effectively extract the feature points of curved laser stripes under strong noise conditions. This paper proposes a Conditional Generative Adversarial Network(CGAN)--based anti-interference recognition method for welding images. The generator adopts an improved U-Net+ + structure, adds a Multi-scale Channel Attention module (MS-CAM), introduces Deep Supervision, and proposes a Multi-output Fusion strategy (MOFS) in the output result to enhance the image inpainting effect; the discriminator uses PatchGAN. The center of the laser stripe is obtained using the grayscale center of mass method and then combined with polynomial fitting to extract the feature points of the weld seam. The experimental results show that the PSNR of the inpainting image is 26.24 dB, the SSIM is 0.98, and the LPIPS is 0.032. The centerline of the inpainting image and the centerline of the noise-free image laser stripe are fitted with a curve. The error of centerline feature points is no more than 5 %, confirming the superiority and feasibility of the method.