用物理先验提升高速撞击碎片追踪精度,助力航天安全分析
DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging
- 基于关键点匹配扩展通用拓扑追踪框架,融入物理知识
- 显著提升碎片质量与速度分布预测准确性,符合实验数据
- 适用于不同撞击角度,帮助专家识别碎片演化规律
本文介绍DebrisTracer框架,用于在高速撞击高速成像中可靠追踪碎片。此类噪声大、特性强的数据集记录了超高速弹丸撞击靶材后产生的大量碎片喷射过程。准确估算碎片质量与速度分布对航空航天应用至关重要。我们通过引入领域知识和物理假设,扩展了现成的拓扑追踪框架(基于关键点提取与匹配),实现了自动、准确且可解释的碎片追踪,支持复杂时空现象的可视化分析。大量实验表明,该方法在物理验证上优于领域专家常用工具,特别是在预测实验喷射质量与坑深分布方面表现优异。我们在多种撞击角度和物理场景下展示了该方法的适用性,其统计结果可帮助视觉识别碎片群体中的不同演化阶段,验证并优化了专家先前的预期。数据库与C++实现代码已公开于:https://github.com/tloloum/DebrisTracer。
原文摘要 · Abstract (English)
This application paper presents DebrisTracer, a framework for the reliable tracking of debris in hypervelocity impact fast imaging. These noisy and highly specific datasets capture the ejection of a large number of debris fragments after the impact of a projectile launched at hypervelocity into a target material. The reliable estimation of debris mass and speed distributions is of major importance in aerospace applications. We document how to extend an off-the-shelf topology tracking framework based on critical point extraction and matching, in order to incorporate domain knowledge and physical assumptions. Our approach automatically produces an accurate and reliable debris tracking, enabling an interpretable visual analysis of this complex space-time phenomenon. Extensive experiments demonstrate the accuracy improvements provided by our approach over established tools used by domain experts in terms of physical validation, specifically via the prediction of the experimental ejected mass and crater depth profiles. We illustrate the utility of our approach across several use cases (with varying impact angles and physics). We show that our statistical summaries enable the visual identification of distinct regimes within the debris population, corroborating and refining prior expectations of domain experts. Our database and our C++ implementation are available at this address: https://github.com/tloloum/DebrisTracer.
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