Yuji Miao , Wei Chen , Lu Tian , Xiaodong Shi , Yuanyuan Zhou , Xiangyong Fan , Jiayi Ma , Jin Wang
{"title":"基于深度学习的轨迹检测技术在中子个人剂量监测研究中的应用","authors":"Yuji Miao , Wei Chen , Lu Tian , Xiaodong Shi , Yuanyuan Zhou , Xiangyong Fan , Jiayi Ma , Jin Wang","doi":"10.1016/j.radmeas.2025.107526","DOIUrl":null,"url":null,"abstract":"<div><div>This study introduces the application of the object detection paradigm (Faster R-CNN) to fast neutron track detection using deep learning algorithms. Neutron dose measurements were conducted using CR-39 detectors, and two methods-morphological features-based detection and deep learning (Faster R-CNN) were evaluated. The deep learning method demonstrated superior accuracy, particularly in identifying small tracks and impurity points, achieving a correct-count rate of 98.7 %. Additionally, it significantly improved detection speed compared to manual and morphological feature-based methods. The performance of the neutron personal dosimetry system, which incorporates the deep learning approach, was validated through linearity validation, coefficient of variation analysis, and dose verification. Due to its fully automated nature, this method can reduce measurement uncertainty and can be extended to recognize other types of particle tracks.</div></div>","PeriodicalId":21055,"journal":{"name":"Radiation Measurements","volume":"189 ","pages":"Article 107526"},"PeriodicalIF":2.2000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of deep learning-based track detection technique in neutron personal dose monitoring research\",\"authors\":\"Yuji Miao , Wei Chen , Lu Tian , Xiaodong Shi , Yuanyuan Zhou , Xiangyong Fan , Jiayi Ma , Jin Wang\",\"doi\":\"10.1016/j.radmeas.2025.107526\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study introduces the application of the object detection paradigm (Faster R-CNN) to fast neutron track detection using deep learning algorithms. Neutron dose measurements were conducted using CR-39 detectors, and two methods-morphological features-based detection and deep learning (Faster R-CNN) were evaluated. The deep learning method demonstrated superior accuracy, particularly in identifying small tracks and impurity points, achieving a correct-count rate of 98.7 %. Additionally, it significantly improved detection speed compared to manual and morphological feature-based methods. The performance of the neutron personal dosimetry system, which incorporates the deep learning approach, was validated through linearity validation, coefficient of variation analysis, and dose verification. Due to its fully automated nature, this method can reduce measurement uncertainty and can be extended to recognize other types of particle tracks.</div></div>\",\"PeriodicalId\":21055,\"journal\":{\"name\":\"Radiation Measurements\",\"volume\":\"189 \",\"pages\":\"Article 107526\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiation Measurements\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1350448725001556\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NUCLEAR SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiation Measurements","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1350448725001556","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Application of deep learning-based track detection technique in neutron personal dose monitoring research
This study introduces the application of the object detection paradigm (Faster R-CNN) to fast neutron track detection using deep learning algorithms. Neutron dose measurements were conducted using CR-39 detectors, and two methods-morphological features-based detection and deep learning (Faster R-CNN) were evaluated. The deep learning method demonstrated superior accuracy, particularly in identifying small tracks and impurity points, achieving a correct-count rate of 98.7 %. Additionally, it significantly improved detection speed compared to manual and morphological feature-based methods. The performance of the neutron personal dosimetry system, which incorporates the deep learning approach, was validated through linearity validation, coefficient of variation analysis, and dose verification. Due to its fully automated nature, this method can reduce measurement uncertainty and can be extended to recognize other types of particle tracks.
期刊介绍:
The journal seeks to publish papers that present advances in the following areas: spontaneous and stimulated luminescence (including scintillating materials, thermoluminescence, and optically stimulated luminescence); electron spin resonance of natural and synthetic materials; the physics, design and performance of radiation measurements (including computational modelling such as electronic transport simulations); the novel basic aspects of radiation measurement in medical physics. Studies of energy-transfer phenomena, track physics and microdosimetry are also of interest to the journal.
Applications relevant to the journal, particularly where they present novel detection techniques, novel analytical approaches or novel materials, include: personal dosimetry (including dosimetric quantities, active/electronic and passive monitoring techniques for photon, neutron and charged-particle exposures); environmental dosimetry (including methodological advances and predictive models related to radon, but generally excluding local survey results of radon where the main aim is to establish the radiation risk to populations); cosmic and high-energy radiation measurements (including dosimetry, space radiation effects, and single event upsets); dosimetry-based archaeological and Quaternary dating; dosimetry-based approaches to thermochronometry; accident and retrospective dosimetry (including activation detectors), and dosimetry and measurements related to medical applications.