Solder Paste Segmentation Method Based on RGB and 3-D Height Modality Feature Fusion

In surface mount technology, solder paste printing quality critically affects product reliability. Accurate segmentation of solder paste regions is essential for defect detection, quantitative analysis, and process optimization. Traditional threshold-based methods lack robustness under varying surface textures and lighting, while deep learning approaches require large annotated datasets and expensive hardware, limiting their use in cost-sensitive manufacturing. We propose a fast, annotation-free segmentation framework based on parameteric multimodal learning, integrating RGB color with 3-D height data. Height priors generate an initial mask, followed by a lookup table–based parameteric color model that adapts to different printed circuit board types and batches. A convolutional feature fusion operator then constructs a joint height–color probability space, suppressing interference from substrate variations and uneven illumination, yielding a refined probability map for final segmentation. Tests on a 3D-solder paste inspection industrial dataset achieve 96.0% mean intersection over union and 98.8% pixel accuracy, matching state-of-the-art deep learning performance while greatly improving efficiency and suitability for real-world deployment without annotated data.