Computer vision-based neural networks for radioisotope identification in urban environments
Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements. We present a machine learning-based approach to this problem that converts list-mode gamma-ray data into two-dimensional waterfall spectrograms and applies computer vision architectures to the resulting images. Rather than treating waterfalls as conventional images, we employ a representation where consecutive time spectra can form input channels, similar to RGB channels in color images. This representation encodes both spectral and...
| Authors | Masen Bachleda, Alea Minar, Ayush Panigrahy et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 30 June 2026 |
| Last updated at source | 30 July 2026 |
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