Robotik Uygulamalar İçin Derin Öğrenme Tabanlı Nesne Tespiti ve Sınıflandırması

Year 2020, , 205 - 213, 15.06.2020

Ferdi Özbilgin , Cengiz Tepe

https://doi.org/10.31466/kfbd.734393

Cited By: 2

Abstract

Görüntü içerisindeki nesnelerin tespit edilmesi ve sınıflandırılma uygulamaları her geçen gün artmaktadır. Bu çalışmada da robotik uygulamalarda da kullanılabilecek bir nesne tespiti ve sınıflandırılması uygulaması gerçekleştirilmiştir. Alexnet Evrişimsel Sinir Ağları (ESA) mimarisi ve Bölgesel Evrişimsel Sinir Ağları (B-ESA) algoritması ile gerçekleştirilen çalışmada yedi farklı nesne sınıfı seçilmiştir. Veri setindeki 684 eğitim verisi etiketlenerek ağın eğitilmesinde kullanılmıştır. 226 test görüntüsü eğitilen ağda test edilmesi sonucunda her sınıfa ait doğru tahmin değerleri ve toplam doğruluk değerleri bulunmuştur. Sınıflara ait tahminlerde en düşük %85,74 ve en yüksek %100 değerlerine ulaşılmıştır. Tüm test verileri için doğruluk değeri %93,81 bulunmuştur.

Keywords

Nesne tespiti, Sınıflandırma, Derin öğrenme

Deep Learning Based Object Detection and Classification for Robotic Applications

Abstract

The detection and classification applications of the objects in the image are increasing day by day. In this study, an object detection and classification application, which can also be used in robotic applications, has been realized. Seven different object classes were selected in the study conducted with Alexnet Evolutionary Neural Networks (CNN) architecture and Regional Convolutional Neural Networks (R-CNN) algorithm. 684 training data in the data set were labelled and used to train the network. As a result of testing 226 test images in the trained network, correct predictive values and total accuracy values of each class were found. The lowest estimates of 85.74% and the highest 100% were reached in the estimates of the classes. The accuracy value was 93.81% for all test data.

Keywords

Object detection, Classification, Deep learning

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