使用卷积神经网络来支持检查人员在带物理适应性比较
Logan Lang1, Pedram Tavadze2, Meghan Prusinowski2
1West Virginia University, Department of Physics and Astronomy, Morgantown, WV 26506, USA.
Forensic science international
|November 21, 2023
概括
本研究介绍了ForensicFit,这是一种用于带物理适应性分析的机器学习模型. 该模型准确地比较带边缘,帮助法医检查和潜在的其他材料分析.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 管道带的物理适应性检查在法医调查中至关重要.
- 目前的方法依赖于手动分析,这可能是主观的,耗时的.
- 需要客观的,自动化的方法来支持法医案例工作.
研究的目的:
- 开发和评估一种机器学习模型,用于客观地检查带的物理适应性.
- 介绍ForensicFit套件用于磁带图像预处理和数据库管理.
- 为了证明深度神经网络的可行性,用于自动材料比较.
主要方法:
- 使用ForensicFit包构建和训练一个卷积神经网络 (CNN).
- 采用近2000张磁带和4000张不同质量级别和分离方法 (剪刀切割,手撕) 的数据集.
- 实施像素重要性方法以确定决策特征.
主要成果:
- 在CNN模型准确地预测适合/不适合类别的带边缘.
- 性能因带质量和分离方法而异:对于中/高质量的剪切带和低质量的手撕带,精度更高.
- 像素重要性分析证实了与分析师识别的特征如边缘形态和支持模式的对齐.
结论:
- 机器学习,特别是深度神经网络,可以为带物理适应性检查提供客观支持.
- 法医Fit套件提供了一个可行的方法来构建物理适应数据库和自动比较.
- 这种方法有可能应用于其他法医材料.
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