- By:
- Van Exel, Kimberly D; Khristy, Joel J; Moresco, Pablo D; Karnowski, Thomas P; Sullivan, Haley E; Sherchan, Samendra
- Page Number:
- 140480-140480
- Volume:
- 14048
- Book Title:
- Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXVII
- Publication Date:
- June 25, 2026
- Publisher Location:
- SPIE, Washington, United States of America
- Conference Name:
- SPIE Defense + Security
- Conference Location:
- Washington, District of Columbia, United States of America
- Conference Sponsor:
- SPIE
- View DOI Listing:
- https://doi.org/10.1117/12.3094013
Abstract
The response to the effects of nuclear detonations is supported by models that describe the evolution of the nuclear fireball and cloud and the associated transport of active debris. Validation of those descriptions relies on data from the nuclear test operations. Video records of those events offer a rich source of information that was exploited to a limited extent in historic analyses. Computer vision and machine learning techniques are powerful tools that can be used to increase the number of measurements that can be obtained from those films. In this work, we apply computer vision techniques to automatically track the temporal evolution of the nuclear fireball. In particular, we apply You Only Look Once 11 (YOLO11) and Segment Anything Model 2 (SAM2) in combination with minimal human intervention to digitized versions of the original nuclear test films. As part of the proposed workflow, the YOLO11 model is applied to films to determine bounding boxes for the fireball within each frame. These are then used as inputs to SAM2, which uses image segmentation to determine the fireball boundaries and their temporal evolution. We assess the accuracy of our approach by using it to determine the energy released during the Trinity nuclear test and comparing the results with previous analyses based on manual measurements.
