基于边缘增强弱监督K-means的大口径望远镜拼接探测器点云分割

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关键词：大口径光电望远镜；拼接探测器；点云分割；边缘检测；聚类中图分类号：TP181；TH751 文献标识码：Adoi：10.37188/OPE.20253321.3331 CSTR：32169.14.OPE.20253321.3331

Abstract：To satisfy the requirements for high-efficiency and high-precision flatness assessment of detectors used in large-aperture，wide-field telescopes，a K-means clustering segmentation method integrating edge-information constraints is proposed for mosaic detectors. Employed as a preprocessng step prior to flatness evaluation，the method reliably extracts regular structural regions and thereby enhances the accuracy and stability of subsequent flatness-index computations. It is applicable to detector inspection across multiple stages and operating conditions. First，the structural characteristics of the spliced detector were analyzed to develop a dedicated point-cloud processing procedure. Edge-gap features were extracted and edge continuity was reinforced. An adaptive initialization of cluster centers within closed regions was implemented to avoid instability arising from random initialization.Furthermore，edge-penalty terms and height-anomaly detection mechanisms were incorporated into the K-means objective function. Through iterative optimization，refined segmentation of individual detector point clouds was achieved.Experimental results indicate that centroid initialization within closed regions reduces the average number of iterations and removes the need for manual preseting of cluster counts. Compared with conventional methods，the incorporation of edge constraints and height-anomaly detection improves boundary-matching accuracy by more than 50% . The proposed method effectively segments point clouds of spliced detector datasets，offering an efficient segmentation approach for the development and deployment of spliced detectors in largeaperture telescopes.

Key words： large-aperture optical and infrared telescope；segmented detector; point cloud segmentation; edge detection；clustering

1引言

随着天文学的迅速发展，大口径、大视场和宽波段的天文光学望远镜逐渐成为解决前沿科学问题的关键技术手段[1]。（剩余16196字）