用机器学习区分枪击与撞击血迹，提升刑侦分析效率
From Images to Detection: Machine Learning for Blood Pattern Classification
- 提取血迹特征+数据整合+提升分类器，构建高效识别模型
- 准确区分枪击与撞击血迹，支持犯罪现场重建
- 适合刑侦、法医及计算机视觉交叉研究者参考
血迹图案分析（BPA）通过研究血迹的大小、形状和分布，帮助理解其形成机制，为犯罪现场重建和嫌疑人位置推断提供依据。本文聚焦于区分枪击血迹与撞击血迹两类典型模式。通过设计精细的单个血迹特征，采用有效的数据整合方法，并选择增强型分类器，构建出兼具高准确率与高效率的识别模型。此外，研究还结合以往文献数据，探讨了当前BPA面临的挑战与未来发展方向。
原文摘要 · Abstract (English)
Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distribution. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, we have developed a model that excels in both accuracy and efficiency. In addition, we use outside data sources from previous studies to discuss the challenges and future directions for BPA.
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