Analysis of Image Formation Laws and Enhancement Methods for Weld Seam Defects Based on Infrared and Magneto-Optical Sensor Technology

Welding defects have a significant influence on welding quality and structural strength, and the rapid and accurate detection of welding defects is required. In order to achieve this goal, it is imperative to create corresponding high-quality datasets. However, capturing image information through a single sensor presents certain limitations. In this study, a magneto-optical imaging device and an infrared thermal imaging device were combined to collect images of resistance spot welding samples. The imaging principles of magneto-optical imaging device and the infrared thermal imaging device are discussed, and the possible factors affecting the imaging modes are analyzed. By synthesizing the 3D gray image, the gray histogram, and inherent image features, the imaging rules of magneto-optical image and the infrared image of resistance spot welding samples have been summarized. Under the guidance of these two image types and imaging modes, image enhancement technology has been utilized to optimize the quality of sample images. The Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Universal Image Quality Index (UIQI) indicators were used to evaluate the optimization quality of the enhanced images. Compared with Histogram Equalization (HE), the Gamma transform, Brightness Preserving Bi-Histogram Equalization (BPBHE), and the Digital Detail Enhancement (DDE) method, the scores of the enhanced infrared images showed improvement across all indicators. The magneto-optical image yielded the best results in the PSNR index, while the other two indices showed only moderate performance. The image dataset, enhanced with appropriate image enhancement techniques, can be utilized for further research into magneto-optical and infrared image information fusion and welding defect identification.