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• Arka-Yüz Yazılım Geliştirme Pr.
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Araştırmacılar(4)

• Altınkaya, Serdar
• Güman, Zülküf
• Şahin, Sümeyye
• Turan, Sedat

• Conference ObjectYalnız künye2021/r/ebyu/14407
  Real-Time Puddle Detection Using Convolutiona Neural Networks with Unmanned Aerial Vehicles

  Bilge Han Taş, Mehmet; Coşkun Irmak, Muhammed; Turan, Sedat; Haşıloğlu, Abdulsamet

  The study was carried out in order to enable systems witli weak processing power and motion to detect objects using cloud services. In addition, the dataset is expanded by continuous labeling to create big data. In the study, it is aimed to detect objects using cloud-based deep learning methods with an unmanned aerial vehicle (UAV). In the study, training processes were carried out with Google Colaboratory, a cloud service provider. The training processes are a YOLO-based system, and a convolutional neural network was created by revising the parameters in line with the needs. The convolutional neural network model provides communication between neurons in the convolutional layers by bringing the image data to the desired pixel ranges. Unlabeled pictures are included in the training by being tagged. In this way, it is possible to continuously enlarge the data pool. Since the microcomputers used in UAVs are insufficient for these processes, a cloud-based training model has been created. As a result of the study, cloud-based deep learning models work as desired. It is possible to show the accuracy of the model with the low losses seen in the loss functions and the mAP value. Graphic cards with high processing power are needed to provide training. It is essential to use powerful graphics cards when working on image data. Cost reduced by using cloud services. The training was accelerated and high-rate object detections were made. YOLOv5x was used in the study. It is preferred because of its fast training and high frame rate. Recall 80% Precision 93% mAP 82.6% values were taken.

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• Conference ObjectYalnız künye2021/r/ebyu/14402
  Emotion Analysis from Facial Expressions Using Convolutional Neural Networks

  Coşkun Irmak, Muhammed; Bilge Han Taş, Mehmet; Turan, Sedat; Haşıloğlu, Abdulsamet

  In order to better understand human behavior, the emotional content of human facial expressions needs to be accurately analyzed and interpreted. While the perception of faces and facial expressions is a natural skill for humans, it still poses great challenges for computer systems. These difficulties result from the non-uniformity of the human face and differences in conditions such as lighting, shadows, face pose and orientation. Deep learning models, especially Convolutional Neural Networks (CNNs), have great potential to deal with these challenges due to their powerful automatic feature extraction and computational efficiency. In this study, a CNN model is proposed to classify seven different emotions (angry, disgust, fear, happy, sadness, surprise and neutral) using the FER-2013 dataset. With the proposed model, 70.62% accuracy on the training data and 70% on the test data has been achieved. The loss value was found to be 0.80 at the training stage and 0.86 at the testing stage.

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• ArticleAçık Erişim2018/r/ebyu/22567
  Analysis of the agricultural product information by processing the images obtained with the help of a mobile robot

  Turan, Sedat; Aydın, Tolga

  Mobile robots, a kind of robotic system, are now being used ever more in different areas of daily life. In this study, it is aimed to determine and analyze tomato plant fruits by image processing by using computer or mobile robot which can be controlled automatically by facial recognition in agricultural areas. The determination of the locations of the fruits, the edge measurements, the area size and the color saturation were obtained by the color detection and tracking image processing method and the approximate weight information about the user's fruit was given with the data set created before. The study demonstrated the feasibility of harvestable fruit-related information with agricultural robots based on computer systems for agricultural research centers or large farmers.

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