An SVM-Based High-Precision Reconstruction Algorithm for High-Power Laser Beam Spots with Large Divergence Angles

Lasers are a key enabling technology across numerous engineering and scientific fields, especially in high-energy laser systems for defense, materials processing, and fusion research, where precise characterization of high-power, large-divergence-angle laser spots is critical. However, the inherent properties of high-power, large-divergence-angle lasers—such as large spot area and strong intensity contrast—pose real obstacles to existing methods, which often suffer from low accuracy and inefficiency. In this paper, a flat-field correction technique was proposed for the CCD to reduce the distortions produced by the non-uniform response of the sensor in spot measurements. Then, a spot recognition algorithm based on support vector machines was developed, which can effectively and accurately locate and identify laser spots with limited training samples and computational resources, achieving a classification accuracy of over 98.11%. Additionally, an efficient correction approach is proposed to assess the spot intensity and shape with high accuracy even at large tilt angles. Experimental results show that this proposed approach can measure the high-power laser spot with a large divergence angle precisely and efficiently, and improves both the measurement precision and operational efficiency remarkably.