高强钢的组织结构如晶粒尺寸、相比例等是决定其最终力学性能韧性、塑性、疲劳性能等的关键因素。 传统的金相分析方法存在主观性强、定量化不足等局限。 为此，需探索并评估机器视觉技术在高强钢显微组织自动识别、定量分析及其与力学性能关联性研究中的应用潜力与影响。 通过开发并优化基于深度学习的图像分割与特征提取算法，实现了对复杂组织的高精度、自动化识别与定量表征。进一步，重点研究了利用提取的组织特征参数(如相比例、相含量)建立预测高强钢关键力学性能(抗拉强度、伸长率)的机器学习模型。 通过上述技术应用实例，提供了一种调控组织与性能的影响分析思路。

The microstructure of high-strength steel (HSS), such as grain size and phase fraction, is a key factor determining its final mechanical properties, including toughness plasticity, and fatigue performance. Traditional metallographic analysis methods suffer from limitations such as strong subjectivity and insufficient quantification. To this end, it is necessary to explore and evaluate the application potential and impact of machine vision technology in the automatic recognition of HSS microstructures, quantitative analysis, and the study of its correlation with mechanical properties. A deep learning-based image segmentation and feature extraction algorithm was developed and optimized, achieving high-precision, automated recognition and quantitative characterization of complex microstructures. Furthermore, it focused on establishing machine learning models to predict key mechanical properties of HSS (tensile strength, elongation) using extracted microstructural feature parameters (e.g., phase fraction, phase content). Through the implementation of these technologies, an analytical approach for influencing microstructure and property regulation is provided.