AI-Based Large-Area Nuclear Particle Track Analysis System
A Region-based Convolutional Neural Network (R-CNN) model was developed for automated analysis of alpha particle tracks. Columbia Resin 39 (CR-39) nuclear detectors were exposed to a 1-μCi Americium-241 (241Am) alpha particle source and subsequently analyzed through large-area Scanning Electron Micr...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573325003067 |
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| Summary: | A Region-based Convolutional Neural Network (R-CNN) model was developed for automated analysis of alpha particle tracks. Columbia Resin 39 (CR-39) nuclear detectors were exposed to a 1-μCi Americium-241 (241Am) alpha particle source and subsequently analyzed through large-area Scanning Electron Microscopy (SEM) imaging over the irradiated area. The R-CNN model was trained on annotated SEM images and achieved a total count accuracy of 95 % with a precision of 94 %, recall of 90 %, and F1-score of 92 % after preprocessing with Canny edge detection to enhance detection accuracy and computational efficiency. The automated analysis methods reduced analysis time by a factor of 1,000, requiring less than 10 min of user intervention. This approach represents a significant advancement in particle track analysis efficiency and accuracy for large-scale datasets. |
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| ISSN: | 1738-5733 |