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

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.