Marine debris detection using visual geometry group 19 and residual network 50

Sanjai P.; Talal Bonny; Nida Nasir; Mohammad AlShabi; Ahmed Al Shammaa

doi:10.1117/12.2664012

12 June 2023 Marine debris detection using visual geometry group 19 and residual network 50

Sanjai P., Talal Bonny, Nida Nasir, Mohammad AlShabi, Ahmed Al Shammaa

Proceedings Volume 12543, Ocean Sensing and Monitoring XV; 125430S (2023) https://doi.org/10.1117/12.2664012
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Marine pollution is a major environmental hazard and a serious healthcare, economic, and social issue. Machine learning (ML) and deep learning (DL) techniques can be used to automate marine waste removal and make the cleanup process more efficient. The proposed study uses image classification to help categorize the level of marine pollution in ocean underwater regions. The performance of two deep convolutional neural networks (VGG19 and ResNet50) is investigated in this study and VGG19 reported an accuracy of 98.1%.

Conference Presentation

Citation Download Citation

Sanjai P., Talal Bonny, Nida Nasir, Mohammad AlShabi, and Ahmed Al Shammaa "Marine debris detection using visual geometry group 19 and residual network 50", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430S (12 June 2023); https://doi.org/10.1117/12.2664012

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