Optimization of laser spot edge extraction and localization based on multi-scale adaptive convolution

Keya YuanLin Li^*

• Beijing Union University, Beijing, China

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 sub-pixel accuracy across diverse experimental conditions, particularly for irregular spots and low signal-to-noise scenarios. This paper 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) sub-pixel localization through Gaussian surface fitting and gradient extremum analysis. Extensive experiments demonstrate the method's superior performance, achieving 0.12-pixel RMSE on standard Gaussian beams (vs. 0.38 for Canny), maintaining 0.15-pixel accuracy with aberrated spots, and showing remarkable robustness at 5dB 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.