We introduce Next Generation Model Trainer (NGMT), a novel software tool designed for interactive training of machine learning models to support the review of surveillance images in nuclear safeguards. This domain poses unique challenges for machine learning applications, including the lack of pretrained models for relevant objects, high variability in objects and activities of interest between facilities, and the time-consuming process of creating labelled training data. To address these challenges, our approach involves training a dedicated model for each surveillance camera and area of interest, combining a pretrained deep vision model with a lightweight binary classifier trained ad-hoc for the specific task. The system leverages active learning and label propagation to enable efficient model training. This paper provides an overview of our methodology and presents the results of a validation campaign conducted at the nuclear inspectorates
CASADO COSCOLLA Alvaro;
SANCHEZ BELENGUER Carlos;
WOLFART Erik;
SEQUEIRA Vitor;
ANGORRILLA BUSTAMANTE Carlos;
VENDRELL VIDAL Eduardo;
PEKKARINEN Juha;
ROCHA Joao Gualberto;
RUUSKA Kai;
BELLIDO VERA Elena;
THOMAS Maikael;
POLLACK Alex;
ROCCHI Simone;
BALASANKARAN Nivetha;
JOHN Melvin;
MOESLINGER Martin;
2026-08-12
PUBLICATIONS OFFICE OF THE EUROPEAN UNION
JRC146037
1977-5296 (online),
https://publications.jrc.ec.europa.eu/repository/handle/JRC146037,
10.3011/ESARDA.IJNSNP.2026.3 (online),
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