文献类型：期刊文献

英文题名：Optimization of laser spot edge extraction and localization based on multi-scale adaptive convolution

作者：Yuan, Keya[1];Li, Lin[2]

第一作者：Yuan, Keya

通讯作者：Li, L[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Appl Sci Technol, Beijing, Peoples R China

第一机构：北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Sci Technol, Beijing, Peoples R China.|[1141775]北京联合大学应用科技学院;[11417]北京联合大学;

年份：2025

卷号：13

外文期刊名：FRONTIERS IN PHYSICS

收录：;EI(收录号：20254519481391);Scopus(收录号：2-s2.0-105021065831);WOS:【SCI-EXPANDED(收录号:WOS:001608039000001)】；

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This article is supported by the National Key Research and Development Program of China (No. 2022YFB4601100).

语种：英文

外文关键词：multi-scale adaptive convolution; laser spot edge extraction; subpixel localization; Gaussian surface fitting; gradient extremum analysis; feature pyramid architecture; optical measurement precision

摘要：The precise extraction of laser spot edges plays a fundamental role in optical measurement systems, yet traditional methods struggle with noise interference and varying spot characteristics. Existing approaches face significant challenges in achieving robust subpixel accuracy across diverse experimental conditions, particularly for irregular spots and low signal-to-noise scenarios. This article presents a novel multi-scale adaptive convolution framework that integrates three key innovations: (1) dynamic kernel adjustment based on local intensity gradients, (2) hierarchical feature pyramid architecture combining spatial details with semantic features, and (3) subpixel localization through Gaussian surface fitting and gradient extremum analysis. Extensive experiments demonstrate the method's superior performance, achieving 0.12-pixel root mean square error (RMSE) on standard Gaussian beams (vs. 0.38 for Canny), maintaining 0.15-pixel accuracy with aberrated spots, and showing remarkable robustness at 5 dB SNR (0.28-pixel RMSE). The results establish that our hybrid approach successfully bridges physical modeling with data-driven adaptation, delivering unprecedented precision (0.91 temporal-spatial consistency) for laser-based applications ranging from industrial metrology to biomedical imaging. The ablation studies further confirm the critical importance of both multi-scale adaptation (61% accuracy drop when removed) and analytical modeling (0.842 F1-score without Gaussian fitting), providing valuable insights for future edge detection research.