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Register a new userImage classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
تصنيف الصور مع الشبكة العصبية التلافيفية العميقة باستخدام تينسور فلو ونقل التعلم
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جامعة بغداد مقالة
Publication date
2020
fields
Informatics Engineering
and research's language is
العربية
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حققت خوارزمية التعلم العميق مؤخرًا الكثير من النجاح خاصة في مجال رؤية الكمبيوتر.يهدف البحث الحالي إلى وصف طريقة التصنيف المطبقة على مجموعة البيانات الخاصة بأنواع متعددة من الصور (صور الرادار ذي الفجوة المركبةSAR والصور ليست SAR) ، أستخدم نقل التعلم متبوعًا بأساليب الضبط الدقيق في مخطط التصنيف هذا . تم استخدام بنيات مدربة مسبقًا على قاعدة بيانات الصور المعروفهImageNet، تم استخدام نموذج VGG 16 بالفعل كمستخرج ميزات وتم تدريب مصنف جديد بناءً على الميزات المستخرجة .تركز بيانات الإدخال بشكل أساسي على مجموعة البيانات التي تتكون من خمس فئات فئة صور الرادارSAR (المنازل) وفئات الصور ليستSAR (القطط والكلاب والخيول والبشر). تم اختيار الشبكة العصبية التلافيفية (CNN) كخيار أفضل لـعملية التدريب لانها نتجت عن دقة عالية. لقد وصلنا إلى الدقة النهائية بنسبة 91.18٪ في خمس فئات مختلفة. تتم مناقشة النتائج من حيث احتمالية الدقة لكل فئة في تصنيف الصورة بالنسبة المئوية. تحصل فئة القطط على 99.6٪ ، بينما تحصل فئة المنازل على 100٪ وتحصل انواع آخرى من الفئات بمتوسط درجات 90٪ وما فوق.
The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.
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