Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution

Image resolution is crucial to visual measurement accuracy, but on the one hand, the cost of increasing the resolution of the acquisition device is prohibitive, and on the other hand, the resolution of the image inevitably decreases when photographing objects at a distance, which is particularly common in the assembly of large hole shaft structures for pose measurement. In this study, a deep learning-based method for super-resolution of large hole shaft images is proposed, including a super-resolution dataset for hole shaft images and a new deep learning super-resolution network structure, which is designed to enhance the perception of edge information in images through the core structure and improve efficiency while improving the effect of image super-resolution. A series of experiments have proven that the method is highly accurate and efficient and can be applied to the automatic assembly of large hole shaft structures.