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Artificial Intelligence
arXiv (cs.LG) · July 17, 2026

DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging

Théophane Loloum, Fabien Vivodtzev, David Hébert +3 more

Fire a projectile into a target at hypervelocity and it throws off a cloud of fragments. High-speed cameras capture the event, and what aerospace engineers need from that footage is the distribution of debris mass and speed, because that is what determines whether a shield protects a spacecraft.

The imagery is unforgiving. Many fragments, all moving extremely fast, in noisy footage from a highly specific instrument, so the general-purpose tracking methods developed on everyday video have little to work with.

What makes this a useful application paper is that the authors document how they extended an off-the-shelf tracker rather than presenting a bespoke system. That is the more transferable contribution: the next team facing a similarly odd domain learns which adaptations were needed and why, instead of receiving a black box tuned to one experiment.

From the arXiv (cs.LG) 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…


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