At border crossings around the world, plastic scintillator-based radiation portal monitors (RPMs) are employed to detect the presence of illicit radioactive materials in large trailer trucks. However, the RPM system shows a low energy resolution owing to the large size and physical characteristics of plastic scintillators; and thus, the identification of illicit artificial isotopes from naturally occurring radioactive material is difficult. This study aims to develop an advanced algorithm for radionuclide identification with commercial RPMs based on commercial plastic scintillators to reduce the occurrence of frequent nuisance alarms. Subsequently, machine learning models, namely, a convolutional neural network (CNN) was applied. The spectral distributions of energy weighted spectra were used as features of the CNN model. The energy spectra of 137Cs, 60Co, 226Ra, and 40K measured under static and moving conditions were used to implement the identification model. To evaluate the performance of the implemented model, the F-score was used. The trained CNN model correctly identified most of the radionuclides. That is, despite the theoretical Compton edge energies of 60Co and 40K being similar, the spectral distributions of 40K are distinctively different from those of 60Co. The result demonstrates that the CNN model-based identification algorithm performs robust radionuclide identification, thereby reducing the frequency of nuisance alarms at border crossings. Furthermore, considering that the actual cases of cargo passing by the RPMs are becoming more complicated, the algorithm would need to be continuously improved and trained with more complex scenarios in the future.
Bibliographical notePublisher Copyright:
© 2020 Elsevier Ltd
All Science Journal Classification (ASJC) codes