مقایسه شبکه های عمیق Faster RCNN و RetinaNet جهت تشخیص خودرو در آب‌وهوای نامساعد
محورهای موضوعی : مهندسی برق و کامپیوتریاسر جمشیدی 1 , راضیه سادات اخوت 2
1 - دانشکده فنی مهندسی، دانشگاه علم و فرهنگ، ایران
2 - دانشکده فنی مهندسی، دانشگاه علم و فرهنگ، ایران
کلید واژه: تشخیص شیء, تشخیص خودرو, یادگیری عمیق, سیستم‌های حمل‌ونقل هوشمند, پردازش تصویر در آب‌وهوای نامساعد,
چکیده مقاله :
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.
Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of detection algorithms used in traffic management systems and autonomous cars. In this article, Faster RCNN deep object recognition network with ResNet50 core and RetinaNet network is used and the accuracy of these two networks for vehicle recognition in adverse weather is investigated. The used dataset is the DAWN file, which contains real-world images collected with different types of adverse weather conditions. The obtained results show that the presented method has increased the detection accuracy from 0.2% to 75% in the best case, and the highest increase in accuracy is related to rainy conditions.
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