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توسعه الگوریتم‌های بینایی ماشین برای شناسایی اهداف آلوده پرتوزا در سناریوهای پرتوی دینامیک | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 79، دی 1403، صفحه 281-292 اصل مقاله (1.14 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.33125.2612 | ||
| نویسندگان | ||
| امیرمحمد بیگ زاده* ؛ هادی اردینی | ||
| پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، تهران، ایران | ||
| تاریخ دریافت: 09 بهمن 1402، تاریخ بازنگری: 21 فروردین 1403، تاریخ پذیرش: 09 اردیبهشت 1403 | ||
| چکیده | ||
| شناسایی و نظارت بر آلودگی رادیواکتیو برای اطمینان از ایمنی عمومی و حفاظت از محیط زیست بسیار مهم است. با این حال، شناسایی منابع رادیواکتیو خارج از کنترل در مکانهای شلوغ می‌تواند چالشبرانگیز باشد، به طور مثال بین مسافران یا وسایل نقلیه. این مطالعه یک رویکرد جدید مبتنی بر ترکیب داده‌ها و روش‌های بینایی ماشین برای تشخیص آلودگی پرتوی در میان اجسام متحرک مشابه معرفی می‌کند. در ابتدا، یک الگوریتم حرکتی برای تعریف 5 جسم متحرک با شکل و اندازه یکسان در یک صفحه دو بعدی، با الهام از معادلات حرکت یک ربات چرخدار کوچک استفاده میشود. این اجسام با سرعت ثابتی در صفحه دو بعدی حرکت می‌کنند. متعاقبا، الگوریتم دیگری با استفاده از روش KLT برای استخراج ویژگی‌های مرتبط و ردیابی همان اشیاء از داده‌های تصویر استفاده میشود. با ترکیب داده های سیستم تشخیص پرتو و بینایی ماشین، این الگوریتم یک یا چند هدف آلوده را شناسایی و شی متحرک آلوده را با موفقیت شناسایی می‌کند. این تحقیق یک روش امیدوارکننده را برای افزایش نظارت بر محیط‌های پرتوی ارائه می‌کند و ادغام تصاویر دوربین‌های نظارتی و سیستم‌های تشخیص پرتوی را برای مناطق عمومی و بزرگ پیشنهاد می‌کند. | ||
| کلیدواژه‌ها | ||
| سناریوی پرتوی؛ آشکارسازی آلودگی رادیواکتیو؛ بینایی ماشین؛ همجوشی داده‌ها؛ مهندسی هسته‌ای؛ دوربین‌های نظارتی | ||
| عنوان مقاله [English] | ||
| Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios | ||
| نویسندگان [English] | ||
| Amir Mohammad Beigzadeh؛ Hadi Ardiny | ||
| Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| Radiation scenario, Radioactive contamination detection, Machine vision, Data fusion, Nuclear engineering, Surveillance cameras | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 286 تعداد دریافت فایل اصل مقاله: 237 |
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