Sualtı Nesnelerinin Sınıflandırılmasında Zaman Serisi Görüntü Dönüşümü Yöntemlerinin Yeni Bir Yaklaşımı ve Uygulaması

Year 2021, Volume: 7 Issue: 1, 1 - 11, 30.04.2021

Aybüke Cıvrızoglu Buz , Mustafa Umut Demirezen , Uraz Yavanoğlu

Abstract

Sonar, ses dalgalarını kullanarak boyutu, uzaklığı, yönü ve diğer nesne özelliklerini belirlemek için kullanılır. Denizaltı madenciliği, petrol arama, denizaltı haritalama, balık sürülerinin takibi ve mayın tespitinde yaygın olarak kullanılmaktadır. Makine Öğrenimi araştırmasında, sonar sinyallerini tanımlamak ve sınıflandırmak için kullanılması gereken özellik çıkarma, seçme, algoritma seçimi ve hiper parametre optimizasyonu, uzun yıllardır çalışılan bilimsel problemler olarak görülmektedir. Bu çalışmada, yaygın olarak kullanılan makine öğrenimi algoritmaları ve öznitelik çıkarma süreçleri yerine, su altı nesnelerini yenilikçi bir yaklaşım olarak sınıflandırmak için üç farklı matematiksel dönüşüm önerilmiştir. Zaman serisi formatında bir veri setine uygulanan bu yeni yaklaşım, veriler tek boyutlu verilerden iki boyutlu bir formata dönüştürülmüş ve bu görüntüleri birleştiren yeni bir görüntü oluşturmak için basit bir kanal birleştirme tekniği uygulanmıştır. Yöntemlerin performansı, sonar veri setinde derin öğrenme algoritmaları kullanılarak mayın ve kayaların sınıflandırma sonuçlarıyla ölçülmüştür. Ayrıca klasik algoritmalar ile ve derin öğrenme ile elde edilen performans sonuçları karşılaştırılmıştır. Son olarak, literatürdeki diğer çalışmalarla karşılaşıldığında, önerilen zaman serisi verilerinden görüntüye dönüşümün kanal birleştirme yaklaşımı ile öznitelik çıkarma ihtiyacını ortadan kaldırdığı ve diğerlerine göre üstün sonuçlar elde ettiği görülmüştür.

Keywords

MTF, RP, GAF, Sonar, Derin Öğrenme

A Novel Approach and Application of Time Series to Image Transformation Methods on Classification of Underwater Objects

Abstract

Sonar is used to determine the size, distance, direction, and other object features using sound waves. It is widely used in submarine mining, oil exploration, submarine mapping, tracking fish shoals, and mine detection. In Machine Learning (ML) research, feature extraction, selection, algorithm selection, and hyper-parameter optimization, which should be used to identify and classify sonar signals, are seen as scientific problems studied for many years. In this study, instead of commonly used ML algorithms and feature extraction processes, three different mathematical transformations were suggested to classify the underwater objects as an innovative approach. This novel approach applied on a data set in time-series format, data has been transformed from one-dimensional data to a two-dimensional format and a simple channel merging technique was applied to create a new image joining these images. The methods' performance was measured by the classification results of mines and rocks using deep learning algorithms on the sonar dataset. Moreover, the performance results obtained with deep learning, compared with the classical algorithms. Finally, confronted with other studies in the literature, it has been seen that the proposed time-series data-to-image transformation with a channel-merging approach eliminates the need for feature extraction and achieves superior results against the others.

Keywords

MTF, RP, GAF, Sonar, Deep Learning

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