Multimodal learning enables instant ionizing radiation alerts on unmodified mobile phones for real-world emergency response
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912532935999488 |
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| author | Xie, Yanfeng Cheng, Xingzhi |
| author_facet | Xie, Yanfeng Cheng, Xingzhi |
| contents | In a radiation emergency, every second counts, yet the public rarely has immediate access to dedicated monitoring devices when they are needed most. Here, the first practical mobile phone-based emergency ionizing radiation detection method is presented that operates entirely without requiring camera coverage or additional hardware modifications. Utilizing a multimodal deep learning approach that integrates sparse radiation-induced signal distributions with the brightness patterns, the proposed framework effectively isolates subtle radiation signals from overwhelming visual interference. A hybrid 3D-2D convolutional neural network (CNN) identifies radiation-induced spots from raw mobile phone video, while a multi-layer perceptron (MLP) fuses the radiation signal and brightness maps for the dose rate estimation. The method detects hazardous dose rates (25-280 mRem/h) rapidly within six seconds (accuracy 86-96%), and low-level radiation (-0.6 mRem/h) with extended measurement durations achieves 87% accuracy. The developed method greatly enhances mobile phone radiation detection practicality and shows substantial potential as an accessible radiation emergency detection tool. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal learning enables instant ionizing radiation alerts on unmodified mobile phones for real-world emergency response Xie, Yanfeng Cheng, Xingzhi Applied Physics In a radiation emergency, every second counts, yet the public rarely has immediate access to dedicated monitoring devices when they are needed most. Here, the first practical mobile phone-based emergency ionizing radiation detection method is presented that operates entirely without requiring camera coverage or additional hardware modifications. Utilizing a multimodal deep learning approach that integrates sparse radiation-induced signal distributions with the brightness patterns, the proposed framework effectively isolates subtle radiation signals from overwhelming visual interference. A hybrid 3D-2D convolutional neural network (CNN) identifies radiation-induced spots from raw mobile phone video, while a multi-layer perceptron (MLP) fuses the radiation signal and brightness maps for the dose rate estimation. The method detects hazardous dose rates (25-280 mRem/h) rapidly within six seconds (accuracy 86-96%), and low-level radiation (-0.6 mRem/h) with extended measurement durations achieves 87% accuracy. The developed method greatly enhances mobile phone radiation detection practicality and shows substantial potential as an accessible radiation emergency detection tool. |
| title | Multimodal learning enables instant ionizing radiation alerts on unmodified mobile phones for real-world emergency response |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2508.08541 |