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تشخیص و ردیابی اهداف هوایی با استفاده از شبکههای عصبی پیچشی | ||
رادار | ||
مقاله 1، دوره 10، شماره 2 - شماره پیاپی 28، دی 1401 | ||
نوع مقاله: مقاله پژوهشی | ||
نویسنده | ||
علی جاهدسراوانی* | ||
استادیار، دانشگاه پدافند هوایی خاتم الانبیا(ص)،تهران، ایران | ||
تاریخ دریافت: 17 شهریور 1401، تاریخ بازنگری: 26 آذر 1401، تاریخ پذیرش: 11 دی 1401 | ||
چکیده | ||
سیستمهای تشخیص و ردیابی خودکار اهداف هوایی از اهمیت ویژهای در صحنه نبرد برخوردار هستند. این نوع از سیستمها از سنسورهای بصری استفاده کرده، قابلیت نصب بر روی سامانههای مختلف نظامی را داشته و برای کشف و ردیابی اهدافی با ارتفاع پست مناسب هستند. در این مقاله، یک شبکه عصبی پیچشی برای تشخیص نوع اهداف هوایی (هواپیمای باری، نمایشی، جنگنده و موشک) طراحی گردیده و در ادامه ردیابی هدف با استفاده از یک شبکه پیشآموزش دیده (GoogLeNet) و یادگیری انتقالی در قالب شبکه عصبی پیچشی مبتنی بر ناحیه انجام شد. دقت شناسایی اهداف هوایی تعریف شده در دادههای تست برابر با 3/96% میباشد. از طرف دیگر میزان همپوشانی چارچوب واقعی و پیشبینیشده هدف در دادههای تست برای هواپیمای باری و نمایشی، جنگنده و موشک به ترتیب برابر 61/0، 66/0، 64/0 و 51/0 میباشد که نشان از دقت مطلوب مدل توسعه داده شده برای ردیابی اهداف در قابهای متوالی میباشد. | ||
کلیدواژهها | ||
اهداف هوایی؛ تشخیص هدف؛ ردیابی هدف؛ CNN؛ RCNN | ||
عنوان مقاله [English] | ||
Recognition and Tracking of Aerial Targets Using Convolutional Neural Network | ||
نویسندگان [English] | ||
Ali Jahedsaravani | ||
Assistant Professor, Khatam Al Anbia Air Defense University, Tehran, Iran | ||
چکیده [English] | ||
Automatic recognition and tracking systems of aerial targets are of particular importance in the battle field. These types of systems use visual sensors, have the ability to be installed on various military systems, and are suitable for discovering and tracking low-altitude targets. In this manuscript, a convolutional neural network was designed to recognize the type of aerial targets (cargo, aerobatics, fighter and missile) and then target tracking using a pre-trained network (GoogLeNet) and transfer learning in the form of a region with convolutional neural network was done. The recognition accuracy of aerial targets in the test data set is 96.3%. On the other hand, the overlap value between the actual and predicted bounding box of target in the test data set for cargo and aerobatics plane, fighter and missile is 0.61, 0.66, 0.64 and 0.51, respectively, which shows the desirable accuracy of the developed model for targets tracking in consecutive frames. | ||
کلیدواژهها [English] | ||
Aerial Targets, Target Recognition, Target Tracking, CNN, RCNN | ||
مراجع | ||
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آمار تعداد مشاهده مقاله: 283 |