AYDINLATMA ÖZNİTELİĞİ KULLANILARAK EVRİŞİMSEL SİNİR AĞI MODELLERİ İLE MEYVE SINIFLANDIRMA

Abstract

Illumination is a natural or artificial source and it allows objects to be seen. Especially use of illumination for necessary in image processing applications for correct and complete object information captured from images. However type, brightness and position of lighting source change, it also changes the image, color, shadow or size of the object and it causes to appear differently on the object. Therefore, the use of a strong artificial intelligence technique to distinguish images will ease the differentiation of classes. Convolutional Neural Networks (CNN), an artificial intelligence method, is an algorithm that can automatically extract features and easily identify obvious features as learning is provided while training network. In the study ALOI-COL dataset used. ALOI-COL consists of 1000 classes such as food and toys obtained with 12 different color temperatures. Fruit images of 29 classes in the dataset were classified using the CNN architectures AlexNet, VGG16 and VGG19. The images in the dataset were increased with image processing techniques and 51 images of each class created. The study 80-20% and 60-40% training-test examined in two structures. As a result of 50 epochs in the test data classified accuracy as 100% by using AlexNet (80-20%) and VGG16 (60-40%) architectures and 86.49% in VGG19 (80-20%) architecture.