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Subject: search.filters.subject.Neural networks

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    Fine-tuning convolutional neural networks for maritime vessel classification, verification and recognition
    (Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2018) Gürkaynak, Cahit Deniz; Arıca, Nafiz
    Autonomous maritime vessel surveillance systems have enormous implications to national defense and global supply chain. Therefore, ship detection and classification problems have been widely studied for a long time. Most of the studies have used satellite imagery, the real-time satellite imaging access is not public and image resolutions is insufficient for high-quality classification and recognition systems. As an alternative approach, consumer-level surveillance cameras have attracted great attention recently due to its cost-effectiveness and easy installation process. Recently, deep learning has become the state-of-the-art method in computer vision field. Deep network architectures have emerged by surpassing human-level accuracy on image classification problems. Many old but powerful ideas have been revised and applied to these networks in various computer vision problems. However, the applications of deep learning methods in the analysis of maritime vessel images are limited in the literature. In this thesis, we employ the state-of-the-art deep network architectures for maritime vessel classification, verification and recognition problems. In the experiments, the most popular three convolutional neural network architectures; AlexNet, VGGNet and ResNet are used. MARVEL dataset is utilized for benchmark purposes, which contains 2M ship images. Since these networks are very difficult to train and they require lots of training samples, we follow transfer learning approach. The main contribution of this thesis is the implementation, tuning and evaluation of specific applications for maritime vessels domain. For classification task, we conduct experiments on different transfer learning techniques and we investigate their performance by transferring the weights layer by layer. We reach the state-of-the-art results by fine-tuning VGG-16 architecture. For both verification and recognition tasks, we use triplet loss heavily inspired by recent advances in the field of face verification and recognition. We achieve closely comparable state-of-the-art results on MARVEL’s both verification and recognition benchmarks.
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    IR image edge detection using neural network and clustering
    (Bahçeşehir Üniversitesi Ekonomik ve Toplumsal Araştırmalar Merkezi, 2018) Mohammadzadeh Meymandi, Tala; Aydın, Tarkan
    Nowadays image processing and feature extraction methods provide significantly important knowledge about images. The first step for identifying objects in an image is extracting the image properties. Edge detection is one the common features of image processing, because edges include useful information about an image. Although general public may not deal with Infrared images directly, this field is widely benefited in many sciences. Therefore, a proper infrared image edge detection method could result in thorough comprehension. In this study, infrared images are selected for edge detection due to their application in various technologies such as medical, military fields and surveillance purposes. According to the structure of these images, it is not possible to extract their edges using common methods. Therefore, a new method is proposed for edge detection of infrared images. In the proposed method, first the image is segmented by a clustering algorithm. Then, Neural Network algorithm is selected to extract the region of interest among the segmented clusters. In the last step, morphological operators are used to extract the edges from the Region of Interest. For segmentation, two K-means and Mean Shift clustering methods are applied separately, and their cluster features are used as the Neural Network inputs. Pursuant to the advantage of Mean Shift clustering algorithm in cluster number determination this method may be favorable in many cases. The evaluation results of the proposed method and comparison with other available methods indicate the method’s good performance for infrared image edge detection.