Statistical analysis of slip system identification and quantitative study on local plasticity is crucial to understand the collective deformation by the sub-grain-scale slip activities in polycrystalline metallic materials. In this study, an automated framework for identifying slip system and assessing strain localization of slip bands termed ASSISL (automated Slip System Identification and Strain Localization analysis of slip bands) is introduced, using the results from high resolution digital image correlation (HR-DIC), and is demonstrated on 1591 primary $\alpha$ grains of a bimodal Ti-6Al-4V alloy under tensile loading. The framework includes: (1) alignment of electron backscattered diffraction (EBSD) maps with strain field maps from HR-DIC through treatment of EBSD distortion, (2) slip band orientations identification from strain field map of each grain through a Radon-transform-based algorithm, (3) slip system assignment with combined Schmid factor and critical resolved shear stress analysis, (4) quantification of plasticity by slip activities which provides information on the numbers, positions and mean strain of slip bands in each grain. The confidence of ASSISL is validated through comparing current statistical results with existing literature, as well as examining reasons for grains assigned with wrong slip systems, which accounts for 9.6 percent (153 out of 1591) of the primary $\alpha$ grains. A comparison between ASSISL and other HR-DIC-based slip identification methods is also conducted. This framework provides a method for analyzing slip activities in a large number of grains of polycrystalline metals in a time-saving and automated fashion.

滑移系统识别的统计分析和局部塑性的定量研究对于理解多晶金属材料中亚晶粒级滑移活动的集体变形至关重要。在这项研究中，介绍了一种用于识别滑移系统和评估滑移带应变定位的自动化框架，称为 ASSISL（自动滑移系统识别和滑移带应变定位分析），使用高分辨率数字图像相关 (HR-DIC)的结果, 并在 1591 小学展示$\alpha$双峰 Ti-6Al-4V 合金在拉伸载荷下的晶粒。该框架包括：(1)通过处理 EBSD 畸变将电子背散射衍射 (EBSD) 图与来自 HR-DIC 的应变场图对齐，(2) 通过氡变换从每个晶粒的应变场图识别滑带方向基于算法，​​（3）结合 Schmid 因子的滑动系统分配和临界解析剪切应力分析，(4) 通过滑动活动量化塑性，它提供有关每个晶粒中滑动带的数量、位置和平均应变的信息。ASSISL 的信心是通过将当前的统计结果与现有文献进行比较，以及检查谷物分配错误的滑动​​系统的原因来验证的，这占主要数据的 9.6%（1591 中的 153）$\alpha$谷物。还对 ASSISL 与其他基于 HR-DIC 的滑动识别方法进行了比较。该框架提供了一种以省时和自动化的方式分析大量多晶金属晶粒中滑移活动的方法。