Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2018/r/munzur/5262
  Classification of Human Actions in Outdoor Images Using Hybrid Deep Learning Methods

  Tümen, Vedat; Algur, Özlem; Yıldırım, Özal

  In this paper, a hybrid deep learning approach is proposed for automatic classification of human actions in outdoor images. Firstly, the detection of the humans in the image taken from the outdoor environment is provided. For this purpose, YOLO, a pre-trained deep object detection tool widely used in the literature, is used. The Google street view platform was preferred to acquire outdoor images. Action classes were created for the detected humans. These action classes; walking to the right, walking to the left, standing and sitting. Thus, a comprehensive dataset has been created for humans identified from the outdoor images. A convolutional neural network (CNN) model is designed for the automatic recognition of classes. With this designed CNN model, automatic recognition of the human actions is provided. This trained model is used in a hybrid manner with the YOLO object detection system to automatically recognize the human actions in the input images.Within the scope of the article, a four-class dataset was created and performance evaluations of the proposed system were made.

  AtıfTR Dizin1

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• MakaleAçık Erişim2018Scopus SJRQ3/r/munzur/3249
  Recognition of road type and quality for advanced driver assistance systems with deep learning

  Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan

  To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.

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  AtıfScopus22·WoS16·OpenAlex25

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• BildiriAçık Erişim2017/r/munzur/670
  Facial emotion recognition on a dataset using Convolutional Neural Network

  Tümen, Vedat; Söylemez, Ömer Faruk; Ergen, Burhan

  Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.

  AtıfScopus68·OpenAlex83

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• MakaleAçık Erişim2015Scopus SJRQ2/r/munzur/4187
  Automatic classification of harmonic data using k-means and least square support vector machine

  Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup

  In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

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  AtıfWoS1·OpenAlex3

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