Application of deep learning in active alignment leads to high-efficiency and accurate camera lens assembly

Haibin Liu, Wenyong Li, Shaohua Gao, Qi Jiang, Lei Sun, Benhao Zhang, Liefeng Zhao, Jiahuang Zhang, Kaiwei Wang

Abstract: Active alignment plays a crucial role in minimizing decentering errors in opticalsystems and enhancing imaging quality. Traditional alignment techniques typically pay lessattention to alignment speed and require precision apparatuses, such as laser locators, wavefront sensors and so on. To address these issues, this paper proposes a learning-search method thatcombines deep learning with search for achieving eficient alignment with a simple hardwaresystem. With the powerful analytical capabilities of deep learning for images, our method achievesa speed of 9.2 seconds in experiments, which is a 56% improvement over the conventionasearch-based method. In terms of accuracy, it reaches an average weighted modulation transferfunction (WMTF) of 0.594, with a difference from search-based method of no more than 0.003Our method signifcantly improves alignment speed while maintaining accuracy, making itwell-suited for large-scale applications.