通过用于法医调查的机器学习算法,推进汽车识别和从轮胎中区分品牌
Navreet Kaur1, Akanksha Sharma1, Vishal Sharma1
1Institute of Forensic Science & Criminology, Panjab University, Chandigarh 160014, India.
概括
对轮胎痕的法医分析可以将车辆与犯罪现场联系起来. 这项研究使用机器学习和ATR-FTIR光谱来快速分类轮胎样本,帮助刑事调查.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
背景情况:
- 轮胎痕在各种刑事案件中都是关键的证据,包括事故和绑架.
- 这些痕迹通过揭示轮胎品牌和原产地,将车辆与犯罪现场和犯罪者联系起来.
- 目前用于轮胎痕迹分析的方法可能耗时且具有破坏性.
研究的目的:
- 开发一种快速,非破坏性的方法来分类汽车轮胎样品.
- 利用机器学习算法从光谱数据有效识别轮胎品牌.
- 在刑事调查中增强轮胎痕迹证据的法医实用性.
主要方法:
- 收集并分析了来自各种品牌的220个轮胎样.
- 采用了使用钻石晶体的减弱总反射率-里埃变换红外光谱 (ATR-FTIR).
- 在光谱数据上应用预处理技术 (基线校正,平滑,衍生,规范化).
- 在PyCaret框架内利用机器学习算法进行分类.
主要成果:
- 根据ATR-FTIR光谱数据成功分类了轮胎样本.
- 证明了机器学习在区分轮胎品牌方面的有效性.
- 实现了轮胎痕迹证据的高效和非破坏性分析.
结论:
- ATR-FTIR光谱和机器学习的结合方法为法医轮胎分析提供了一个强大的工具.
- 这种方法有助于从痕迹证据中快速准确地识别汽车轮胎的品牌.
- 该研究为依赖轮胎痕迹证据的法医调查提供了宝贵的进步.
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