
מרק לסט
אקדמי בכיר
Image segmentation and classification for fission track analysis for nuclear forensics using U-net model
This study introduces a novel methodology for the detection and classification of fission track (FT) clusters in microscope images, employing state-of-the-art deep learning techniques for segmentation and classification (Elgad in nuclear forensics—fission track analysis—star segmentation and classification using deep learning, Ben-Gurion University, 2022). The U-Net model, a fully convolutional network, was used to carry out the segmentation of various star-like patterns in both single-class and multi-class scenarios.
| שפת פרסום | אנגלית |
| דפים | 2321-2337 |
| כתב עת | Journal of Radioanalytical and Nuclear Chemistry |
| כרך | 333 |
| נושא מספר | 5 |
| סטטוס פרסום | פורסם - 01.05.2024 |
Keywords
Computer vision
Fission track analysis
Holmeland security
Nuclear forensics
Safeguards investigations
U-Net
ASJC Scopus subject areas
Analytical Chemistry
Nuclear Energy and Engineering
Radiology Nuclear Medicine and imaging
Pollution
Spectroscopy
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis