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Deep Neural Network Driven Automated Underwater Object Detection

Ajisha Mathias1, Samiappan Dhanalakshmi1,*, R. Kumar1, R. Narayanamoorthi2

1 Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India
2 Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

* Corresponding Author: Samiappan Dhanalakshmi. Email:

Computers, Materials & Continua 2022, 70(3), 5251-5267.


Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.


Cite This Article

A. Mathias, S. Dhanalakshmi, R. Kumar and R. Narayanamoorthi, "Deep neural network driven automated underwater object detection," Computers, Materials & Continua, vol. 70, no.3, pp. 5251–5267, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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