Derin Ogrenme kullanilarak Ses Sinyali FFT Spektrumlarina dayali Gemi Tipleri Siniflandirilmasi

Year 2023, Volume: 10 Issue: 1, 57 - 65, 31.01.2023

Mustafa Eren Yıldırım

https://doi.org/10.31202/ecjse.1149363

Abstract

Gemi tiplerinin tanınmasi, denizcilik sektöründe ihtiyaç duyulan ve ilgi ceken bir uygulamadir. Literatürdeki çalışmaların büyük bir kısmı kıyı kameraları tarafından çekilen görüntülerin, radar görüntülerinin veya ses özelliklerinin kullanımına odaklanmıştır. Görüntü tabanlı tanıma durumunda, çok sayıda ve çeşitlilikte gemi görüntüsünün toplanması gerekir. Ses tabanlı tanıma durumunda ise, sistemlerin performansi arka plan gürültüsünden olumsuz etkilenebilir. Bu çalışma, görüntü tabanlı bir derin öğrenme ağı ile ses-frekans alanı özelliklerini kullanan bir yöntem sunmaktadir. Yöntem, gemilerin ses kayıtlarının hızlı Fourier dönüşümünü hesaplar ve elde edilen spektrumlari resim formatinda kaydeder. Bu resimler sınıflandırma için ResNet50 ağına verilir. Önerilen yöntemin performansını test etmek için dokuz farklı gemi tipine sahip erisime acik bir veri seti kullanılmıştır. Elde edilen sonuçlara göre onerilen metot %99 siniflandirma basarisi saglamistir.

Keywords

Sinyal isleme, gemi tipi siniflandirma, ResNet50, mel frekansi kepstral katsayilari

Ship Type Recognition using Deep Learning with FFT Spectrums of Audio Signals

Abstract

Ship type recognition has gained serious interest in applications required in the maritime sector. A large amount of the studies in literature focused on the use of images taken by shore cameras, radar images, and audio features. In the case of image-based recognition, a very large number and variety of ship images must be collected. In the case of audio-based recognition, systems may suffer from the background noise. In this study, we present a method, which uses the frequency domain characteristics with an image-based deep learning network. The method computes the fast Fourier transform of sound records of ships and generates the frequency vs magnitude graphs as images. Next, the images are given into the ResNet50 network for classification. A public dataset with nine different ship types is used to test the performance of the proposed method. According to the results, we obtained a 99% accuracy rate.

Keywords

Signal Processing, Ship type recognition, ResNet50, MFCC

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