Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Neural Network and Local Template Matching

With the increasing integration of industrial products, critical components are increasingly being encapsulated within sealed enclosures, making it difficult to measure their actual positions during assembly using contact measurement techniques, often leading to substandard product quality. X-ray imaging provides a non-destructive solution for inspecting internal structures and accurately positioning internal components. However, traditional pose estimation methods based on X-ray imaging rely on projection optimization, which is time-consuming and cannot meet the timely feedback requirements of assembly processes. In this study, we propose a hybrid pose estimation method for industrial X-ray radiography that combines neural networks for initial pose estimation with local template matching for pose refinement. This approach achieves both high accuracy and efficiency in positioning internal targets. We conducted real X-ray imaging experiments on several objects, including a terahertz anode tube model. The mean alignment error was approximately 0.2 mm, lower than the spatial resolution (about 0.25 mm) of the CT images constructed from the same X-ray projections. The computation time for pose estimation of a single object was about 10 s, significantly faster than conventional methods that typically requiring several minutes, making it suitable for timely feedback in industrial assembly processes.