Ocean litter detection using inception transfer learning models

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

doi:10.1117/12.2664014

12 June 2023 Ocean litter detection using inception transfer learning models

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

Proceedings Volume 12543, Ocean Sensing and Monitoring XV; 125430U (2023) https://doi.org/10.1117/12.2664014
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Plastic pollution has emerged as one of the biggest environmentally threatening issues. Using image classification, the proposed study aids in categorizing the level of marine pollution in ocean underwater regions. This study classified the amount of pollution in the ocean using the two variants of Inception Convolutional Neural Network (CNN) models i.e., Inception- ResNet V2, and InceptionV3. High accuracies of up to 96.4% have been reported. This study will help researchers working in the field of water quality detection.

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Citation Download Citation

Sanjai P., Talal Bonny, Nida Nasir, Mohammad AlShabi, and Ahmed Al Shammaa "Ocean litter detection using inception transfer learning models", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430U (12 June 2023); https://doi.org/10.1117/12.2664014

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