Solder Joint Inspection Using Digital Model Priors for Industrial Applications

With the deepening exploration and application of machine vision technology, researchers are actively introducing advanced object detection and recognition methods in the industrial domain, particularly in the critical manufacturing process of welding quality inspection. To address the limitations of traditional solder joint inspection methods,-such as low efficiency, high false alarm rates, and poor adaptability to complex environments,-this study innovatively proposes an industrial visual solder joint inspection algorithm that integrates digital model optimization with deep learning. The core contributions of the algorithm are twofold: first, it refines the conventional image processing pipeline by incorporating prior information from known digital models, thereby enhancing the efficiency and quality of image preprocessing; second, it introduces a multi-scale and multi-template matching strategy coupled with a Faster RCNN three-stage cascade detection framework guided by digital model coordinate Regions of Interest (ROIs), enabling efficient and accurate recognition of solder joints in images. In this process, digital model information is utilized to optimize and improve the underlying detectors. This design not only strengthens the algorithm’s robustness in complex industrial settings but also significantly improves the accuracy and robustness of solder joint detection. Experimental validation on a solder joint dataset constructed in a real manufacturing environment demonstrates the promising detection performance of the proposed approach, offering reliable technical support for real-time monitoring and quality enhancement in industrial automation. This research not only advances the practical application of machine vision in industrial inspection but also lays a solid foundation for the future development of intelligent and precise manufacturing.