在混凝土中的子弹 ricochet 标记平面视图形态:使用机器学习对五种子弹类型和两种距离进行实验性评估
Metin I Eren1,2, Jay Romans3, Robert S Walker4
1Department of Anthropology, Kent State University, Lowry Hall, 750 Hilltop Drive, Kent, OH, USA.
Forensic sciences research
|April 2, 2024
概括
弹子反弹冲击部位形态学可以帮助识别射击调查中的子弹类型. 机器学习分析显示了从冲击痕迹中确定未知子弹的潜力,尽管人类识别可能容易出错.
科学领域:
- 法医科学 法医科学 法医科学
- 弹道学 弹道学 弹道学
- 机器学习应用 机器学习应用
背景情况:
- 在枪击事件中,子弹 ricochets 很常见,为重建提供了有价值的数据.
- 关于子弹 ricochet 撞击点形态的系统研究是有限的.
- 了解反弹痕迹有助于法医调查.
研究的目的:
- 实验性地检查在混凝土中的子弹 ricochet 撞击点的平面视图形态.
- 评估机器学习在基于 ricochet 标记形态的基础上对子弹类型的分类中的有效性.
- 调查子弹类型和射击距离对 ricochet 撞击现场特征的影响.
主要方法:
- 从两个距离使用五种不同类型的子弹在混凝土中产生了297个反弹冲击点.
- 使用随机森林机器学习算法,根据形态维度 (长度,周边与面积比) 来分类子弹类型.
- 员工休假-一个-输出交叉验证来评估分类准确性.
主要成果:
- 形态维度,特别是长度和周边与面积的比率,是分类子弹类型的关键预测因素.
- 0.22 LR子弹产生了最独特的冲击痕迹.
- 分类准确率达到62% (与20%的随机机会相比),当包括射击距离时,增加到66%.
结论:
- 弹子反弹撞击现场形态显示了确定或概率评估未知的子弹类型的潜力.
- 不同的子弹类型之间的形态重叠很大,即使在受控条件下,也表明在现实世界中,人类可能会错误识别.
- 机器学习提供了一个有希望的,数据驱动的方法来分析反弹证据.
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