Konvolüsyonel Sinir Ağına Sahip Akıllı Kontrol Cihazları

Year 2019, Volume: 4 Issue: 2, 54 - 67, 01.12.2019

Zainab Mahmood

Abstract

Bir konvolüsyon sinir ağı
(CNN) bir veya daha fazla konvolüsyon katmanları (genellikle bir alt örnekleme)
ile oluşur ve ardından bir veya daha fazla tam katman, çok katmanlı sinir ağı
olarak izler. CNN mimarisi 2D giriş görüntü (veya konuşma sinyali gibi diğer 2D
giriş) yapısının yararlanmak için tasarlanmıştır. Bu sonuç yerel bağlantılar
ile elde edilir ve statik parçaların çevirisi toplama sonuçları türü tarafından
takip ağırlık ile ilgilidir. CNN'lerin diğer bir avantajı, eğitilmesi daha
kolaydır ve aynı sayıda gizli birimle daha az tam ağ parametrelerine sahiptir.
Bu tezin amacı, yakalama cihazı (kamera) kullanılarak el hareketlerini otomatik
olarak algılayabilen. Konvolüsyonel sinir ağı kullanarak gerçek zamanlı olarak
görüntü işleme sistemini tasarlayıp yürütmektir. Program, konvülsiyonel sinir
ağıyla elle hareket tespiti yapacak; işitme engelli ve görme engelli insanlara
yardım ederek evlerinde cihazlara kontrol etmelerine yardımcı olacak, aynı anda
birçok farklı jesti algılayabilecek ve taleplerini yerine getirmek için onlara
yardımcı olacaktır. Bu program ile el hareketleri kullanarak elektrik, Mekanik
veya elektronik olup olmadığı kontrol edinilebilecektir. Program aynı zamanda
işitme engelli insanlara işaret dilini öğretmek için kullanılabilecektir. Keras
makine öğrenme Kütüphanesi kullanılarak, CNN modelini eğitmek ve oluşturmak
için gerekli yazılım Python’da geliştirilecektir. Ayrıca Python, yakalama
cihazından (kamera) görüntü tanıma taleplerini bekleyen istemci tarafındaki soketi
çalıştırıyor ve fonksiyonları internet üzerinden çıkış birimine (Arduino)
gönderiyordu. Ayrıca 3. parti ortamı (firebase) var, donanımı yazılımla
birleştirmek için ortam olarak çalışıyor.

Keywords

Ardunio, Sign Language, Convolutional Neural Network, Python

Smart Controlling Devices with Convolutional Neural Network

Abstract

A neural network convolutional (CNN) is composed of one
or more convolutional layers (often with a step of sub-sampling) and then
followed by one or more full layers, as in a neural network multilayer. The architecture of CNN is designed to take advantage of
the structure to a 2D input image (or other 2D input such as a speech signal). This result is obtained with local connections and are
relevant to the weight, followed by the type of aggregation results in the
translation of the static parts. Another advantage of the CNNs
so that it is easier to train and they have a fewer number of parameters of the
full network with the same number of hidden units. See all the tutorials on convolution and collecting for more details of
this operation. The goal of this work
is to design and execute a system using Convolutional
Neural Network that capable of automatically detect and recognize hand
gesture through capture and process image in real time by using capture device
(camera) . The program going to be on hand gesture detection with convolutional
neural network , and it can be detect many different types of gestures at same
time that will be used for helping disabled , deaf or blind people that will
helping them to control things in their House and helping them for fulfilling
their demands.With this program
we can control electrical devices, mechanicals or electronics by using hands
gestures . The program also can be used as teaching the deaf peoples to learn
sign language. The code for training and
creating the CNN model will be in Python using the Keras machine learning.

Keywords

Ardunio, Sign Language, Convolutional Neural Network

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