Please use this identifier to cite or link to this item:
Title: Performance analysis of machine learning models for object recognition in underwater video images
Other Titles: Sualti video görüntülerinde nesne tanima amaçli yapay ö?renme modellerinin performans analizi
Authors: Özdilli, B.G.
Arslan, M.B.
Alp, T.
Albayrak, O.
Ünal, P.
Bozkurt, O.
Özbayoğlu, A. Murat
Keywords: Computer vision
Deep learning
Machine learning
Object recognition
Underwater image analysis
Deep learning
Logistic regression
Multilayer neural networks
Object detection
Object recognition
Support vector machines
Underwater equipment
Accuracy level
Classification performance
Image histograms
Learning models
Machine learning models
Multi layer perceptron
Performance analysis
Underwater cables
Learning systems
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this study, our primary aim is to detect different formations, objects on the images taken from various underwater videos. For this purpose, machine learning models such as SVM, multi-layer perceptron, logistic regression that use attributes, image histogram obtained from images were chosen. In addition, Autoencoder and CNN based deep learning models were used directly over images and their performances were compared. According to the results, it was observed that all models were satisfactory and achieved good classification performances. The highest performance was observed in the Autoencoder based deep learning model, which achieved an accuracy level of %95. In the future, we are planning to continue studies to focus on underwater cable tracking and detecting errors and anomalies in underwater cables. © 2021 IEEE.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536
ISBN: 9781665436496
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

checked on Sep 25, 2023

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.