مقایسه شبکه های عمیق Faster RCNN و RetinaNet جهت تشخیص خودرو در آب‌وهوای نامساعد
الموضوعات :یاسر جمشیدی 1 , راضیه سادات اخوت 2
1 - دانشکده فنی مهندسی، دانشگاه علم و فرهنگ، ایران
2 - دانشکده فنی مهندسی، دانشگاه علم و فرهنگ، ایران
الکلمات المفتاحية: تشخیص شیء, تشخیص خودرو, یادگیری عمیق, سیستم‌های حمل‌ونقل هوشمند, پردازش تصویر در آب‌وهوای نامساعد,
ملخص المقالة :
تشخيص وسايل نقليه و رديابی آن، نقش مهمی در اتومبیلهای خودران و سيستم‌هاي حمل‌ونقل هوشمند ايفا می‌کند. شرايط آب‌وهوايی نامساعد مانند حضور برف سنگين، مه، باران و گرد و غبار با کاهش ديد دوربين، محدوديت‌هاي خطرناکی ايجاد کرده و بر عملکرد الگوريتم‌هاي تشخيصی استفاده‌شده در سيستم‌هاي نظارت بر ترافيک و برنامه‌هاي رانندگی خودکار تأثير می‌گذارد. در این مقاله از شبکه عمیق تشخیص اشیای Faster RCNN با هسته 50ResNet و شبکه RetinaNet استفاده شده و دقت این دو شبکه جهت تشخیص خودرو در آب‌وهوای نامساعد مورد بررسی قرار میگیرد. پایگاه داده مورد استفاده، فایل DAWN می‌باشد که شامل تصاویر دنیای واقعی است و با انواع مختلفی از شرایط آب‌وهوایی نامطلوب جمع‌آوری شده‌اند. نتایج به‌دست‌آمده نشان میدهند که روش ارائه‌شده در بهترین حالت، دقت تشخیص را از %2/0 به %75 افزایش داده و بیشترین میزان افزایش دقت نیز مربوط به شرایط بارانی می‌باشد. تمام پردازشها به زبان پایتون و در گوگل کولب انجام شده است.
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