DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging

2026-07-17

AI Summary

This work addresses the challenge of reliably tracking fragment trajectories in high-speed imaging of hypervelocity impacts, where conventional methods often fail under high noise conditions. The authors propose a novel approach that integrates physical constraints with topological tracking, leveraging critical point extraction and matching informed by domain knowledge and dynamical assumptions. Implemented efficiently in C++, the method enables automatic and robust tracking of fragment clouds. It significantly outperforms existing tools in physically meaningful validation metrics—such as ejected mass estimation and crater depth profiling—and further uncovers distinct dynamical regimes within the fragment ensemble. The system supports interpretable spatiotemporal visual analysis, earning endorsement from domain experts for its scientific utility and fidelity.

Abstract

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.

Problem

• hypervelocity impact
• debris tracking
• fast imaging
• mass distribution
• speed distribution

Innovation

• hypervelocity impact
• debris tracking
• topological tracking
• critical point extraction
• physical validation