Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios
Detecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is...
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Semnan University
2024-12-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_9232_84c56927eab8051abd431b7234b22813.pdf |
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| author | Amir Mohammad Beigzadeh Hadi Ardiny |
| author_facet | Amir Mohammad Beigzadeh Hadi Ardiny |
| author_sort | Amir Mohammad Beigzadeh |
| collection | DOAJ |
| description | Detecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is based on data fusion and machine vision methods. The approach detects radiological contamination among similar moving objects. At first, we use the motion algorithm to define 5 moving objects. They are of the same shape and size and in a two-dimensional plane. Their motion equations were inspired by the small wheeled robot. These objects move with the same speed in the plane. Next, with another algorithm based on the KLT method, we extracted related features and tracked the same objects from the image data. The algorithm combines the beam detection system's data and machine vision. It finds one or more infected targets. It successfully detects the infected moving object. This research shows a promising approach to improve monitoring of radiation environments. It suggests integrating surveillance camera images and radiation detection systems for public and large areas. |
| format | Article |
| id | doaj-art-36bd29d2dc1444209ab4d8e88cce6339 |
| institution | Kabale University |
| issn | 2008-4854 2783-2538 |
| language | fas |
| publishDate | 2024-12-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj-art-36bd29d2dc1444209ab4d8e88cce63392025-01-15T08:17:35ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382024-12-01227928129210.22075/jme.2024.33125.26129232Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation ScenariosAmir Mohammad Beigzadeh0Hadi Ardiny1Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, IranRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, IranDetecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is based on data fusion and machine vision methods. The approach detects radiological contamination among similar moving objects. At first, we use the motion algorithm to define 5 moving objects. They are of the same shape and size and in a two-dimensional plane. Their motion equations were inspired by the small wheeled robot. These objects move with the same speed in the plane. Next, with another algorithm based on the KLT method, we extracted related features and tracked the same objects from the image data. The algorithm combines the beam detection system's data and machine vision. It finds one or more infected targets. It successfully detects the infected moving object. This research shows a promising approach to improve monitoring of radiation environments. It suggests integrating surveillance camera images and radiation detection systems for public and large areas.https://modelling.semnan.ac.ir/article_9232_84c56927eab8051abd431b7234b22813.pdfradiation scenarioradioactive contamination detectionmachine visiondata fusionnuclear engineeringsurveillance cameras |
| spellingShingle | Amir Mohammad Beigzadeh Hadi Ardiny Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios مجله مدل سازی در مهندسی radiation scenario radioactive contamination detection machine vision data fusion nuclear engineering surveillance cameras |
| title | Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios |
| title_full | Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios |
| title_fullStr | Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios |
| title_full_unstemmed | Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios |
| title_short | Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios |
| title_sort | development of machine vision algorithms for radioactive contaminated targets detection in dynamic radiation scenarios |
| topic | radiation scenario radioactive contamination detection machine vision data fusion nuclear engineering surveillance cameras |
| url | https://modelling.semnan.ac.ir/article_9232_84c56927eab8051abd431b7234b22813.pdf |
| work_keys_str_mv | AT amirmohammadbeigzadeh developmentofmachinevisionalgorithmsforradioactivecontaminatedtargetsdetectionindynamicradiationscenarios AT hadiardiny developmentofmachinevisionalgorithmsforradioactivecontaminatedtargetsdetectionindynamicradiationscenarios |