Comparison of AlexNet Algorithm with DenseNet Algorithm for Aquatic Debris Detection on Ocean Surfaces

T. Kamal, N. Bharatha Devi

2023

Abstract

This research aims to spot aquatic debris on ocean surfaces using the novel Alexnet algorithm, comparing its performance against the DenseNet algorithm. Materials and Methods: To enhance the detection of aquatic debris, two algorithms such as Novel AlexNet and DenseNet are compared with iteration done for each group as 20 are implemented on the data set with a G-Power of 80% and confidence level of 95% using the clinical software. The dataset used for this research consists of 324 JPG images of marine debris and 724 JPG images of the ocean with varying degrees of clarity, including images with noise. Results: With an accuracy of 93.77%, the Novel AlexNet algorithm identifies and measures objects with more accuracy than the DenseNet algorithm, with 92.91%. It shows that there is no statistical significance difference between the Novel Alexnet Algorithm and DenseNet Algorithm with p=0.530 (Independent Sample T-test p<0.05). Conclusion: The detection of aquatic debris using the Novel AlexNet algorithm provides superior performance compared to the DenseNet algorithm, as concluded from the obtained results.

Paper Citation

in Harvard Style

Kamal T. and Devi N. (2023). Comparison of AlexNet Algorithm with DenseNet Algorithm for Aquatic Debris Detection on Ocean Surfaces . In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 367-372. DOI: 10.5220/0012772400003739

in Bibtex Style

@conference{ai4iot23,
author={T. Kamal and N. Bharatha Devi},
title={Comparison of AlexNet Algorithm with DenseNet Algorithm for Aquatic Debris Detection on Ocean Surfaces },
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={367-372},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012772400003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Comparison of AlexNet Algorithm with DenseNet Algorithm for Aquatic Debris Detection on Ocean Surfaces
SN - 978-989-758-661-3
AU - Kamal T.
AU - Devi N.
PY - 2023
SP - 367
EP - 372
DO - 10.5220/0012772400003739
PB - SciTePress