With the increasing incidence of anterior segment diseases such as glaucoma, the analysis of anterior segment images has received more and more attention. Moreover, the precise localization of the scleral spur (SS) is of great significance for the measurement of anterior segment structural parameters and clinical diagnosis. It presents a two-stage localization network from "coarse" to "fine" for optical coherence tomography images of the anterior segment. First, a ROI region detection network was used to identify ROI regions centered on the scleral spur, and the feature extraction ability of the network was improved by adding the ACmix module to the backbone network. Afterwards, a lightweight network Lite-HRNet was used as a fine-tuning correction network to further extract image detail features, based on heatmap regression to obtain a more accurate scleral process location. Finally, we tested our method on two datasets, reaching an average error of 11.74 and 9.82 respectively. Many experiments show that our method is better than other existing deep learning-based methods and has good detection effect.