:: دوره 7، شماره 4 - ( 12-1398 ) ::
جلد 7 شماره 4 صفحات 99-77 برگشت به فهرست نسخه ها
ارائه یک الگوریتم ترکیبی مبتنی بر یادگیری عمیق و ماشین بولتزمان محدود با هدف قطعه‌بندی معنایی خودرو از تصاویر مادون‌قرمز حرارتی پهپاد
مهدی خوش برش ماسوله ، رضا شاه حسینی*
دانشگاه تهران
چکیده:   (3315 مشاهده)
امروزه پایش وسایل نقلیه زمینی با استفاده از روش‌های پردازش تصویر، یکی از حیطه‌های کاربردی در کنترل ترافیک هوشمند به شمار می‌آید. در این زمینه، به‌کارگیری تصاویر مادون‌قرمز حرارتی پهپاد به دلیل قدرت تفکیک مکانی مناسب، مقرون ‌به ‌صرفه بودن و حجم کمتر تصاویر، یکی از گزینه‌های مطلوب برای هدف پایش وسایل نقلیه است. روش‌هایی که تا به حال برای استخراج وسایل نقلیه از تصاویر حرارتی ارائه شده‌اند، اغلب دارای مشکلاتی نظیر دقت پایین در شناسایی و قطعه‌بندی (مانند روش HOG+SVM) و نیاز به کلان داده‌های آموزشی (مانند روش‌های یادگیری عمیق) است. در تحقیق حاضر، یک مدل جدید با نام SegRBM-Net بر اساس یادگیری عمیق و ماشین بولتزمان محدود ارائه شده است. از جمله ویژگی‌های مدل SegRBM-Net، افزایش دقت شناسایی و قطعه‌بندی وسایل نقلیه از تصاویر حرارتی با استفاده توأم از لایه‌های کانوولوشنی و ویژگی‌های ماشین بولتزمان محدود گوسین - برنولی می‌باشد. این ساختار موجب شده است تا الگوریتم، هدف را با سرعت و دقت بیشتری نسبت به سایر روش‌های یادگیری عمیق پیدا کند. به‌منظور ارزیابی کارایی و دقت روش پیشنهادی، از چهار مجموعه داده مادون‌قرمز حرارتی پهپاد با ویژگی‌هایی نظیر تراکم بالای وسایل نقلیه در صحنه و زاویه دید متنوع استفاده شده است. بر اساس نتایج این تحقیق، مدل SegRBM-Net با دقت میانگین 99 درصد و بهبود سرعت پردازش، نسبت به روش‌های مشابه دارای کارایی مناسبی می‌باشد.
واژه‌های کلیدی: شبکه عصبی کانوولوشنی عمیق، ماشین بولتزمان محدود گوسین - برنولی، قطعه‌بندی معنایی، وسایل نقلیه زمینی، تصاویر مادون‌قرمز حرارتی پهپاد.
متن کامل [PDF 2174 kb]   (1955 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: سنجش از دور
دریافت: 1397/9/24 | پذیرش: 1398/3/25 | انتشار: 1398/12/29
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دوره 7، شماره 4 - ( 12-1398 ) برگشت به فهرست نسخه ها