اصلاح معماری شبکه عصبی کانولوشنال جهت طبقه‌بندی تصاویر آغشته به نویز ضربه
الموضوعات :
محمد مومنی
1
,
مهدی آقاصرام
2
,
علی محمد لطیف
3
,
راضیه شیخ پور
4
1 - دانشگاه یزد
2 - دانشگاه یزد
3 - دانشگاه یزد
4 - دانشگاه اردكان
الکلمات المفتاحية: نویز ضربهشبکه عصبی کانولوشنالطبقه‌بندی تصویرشناسایی نویز,
ملخص المقالة :
نویز ضربه موجب اختلال در فرایند طبقه‌بندی تصاویر توسط شبکه عصبی کانولوشنال می‌گردد. پیش‌پردازش جهت حذف نویز ضربه هزینه‌بر است و تصاویر تخریب‌شده به دلیل عدم بهبود کافی، اثر مخرب در مراحل آموزش و اعتبارسنجی این شبکه دارند. در این مقاله با اصلاح معماری شبکه عصبی کانولوشنال، یک مدل مقاوم در برابر نویز ضربه معرفی می‌شود. روش پیشنهادی، طبقه‌بندی تصاویر نویزی را بدون نیاز به هیچ گونه پیش‌پردازش انجام می‌دهد. لایه تشخیص نویز ضربه در بدنه شبکه عصبی کانولوشنال تعبیه می‌شود و از پردازش مقادیر نویزی جلوگیری می‌کند. برای آموزش مدل پیشنهادی از پایگاه داده 2012- ILSVRC استفاده شده است. نتایج شبیه‌سازی نشان می‌دهد که جلوگیری از تأثیرگذاری نویز ضربه در فرایند آموزش و طبقه‌بندی شبکه عصبی کانولوشنال، دقت و سرعت آموزش شبکه را افزایش می‌دهد. روش پیشنهادی با خطای 24/0 در طبقه‌بندی تصاویر آغشته به نویز ضربه با چگالی 10% بهتر از سایر روش‌های مورد مقایسه می‌باشد. مرتبه زمانی (1)O در اصلاح CNN جهت مقاومت در برابر نویز نشان‌دهنده برتری روش پیشنهادی است.
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