Paper Contents
Abstract
The detection and classification of underwater objects, such as submarines and mines, is of paramount importance for maritime security and defense. Traditional methods of detection and classification in this challenging environment have limitations in terms of accuracy and efficiency. This study introduces a novel approach for Submarine Rock vs. Mine Prediction using Machine Learning, leveraging advanced techniques to enhance the accuracy of target identification. This research project encompasses the development and implementation of a machine learning model designed to discriminate between natural submarine rock formations and man-made naval mines. The model is trained on a diverse dataset of acoustic and sonar data collected from underwater environments, incorporating various types of vessels and underwater geological features.The machine learning model utilizes state-of-the-art algorithms, including deep neural networks and feature engineering, to effectively differentiate between the acoustic signatures of rocks and mines. It demonstrates robust performance in terms of classification accuracy, precision, and recall, contributing to the reduction of false alarms and improved maritime security.
Copyright
Copyright © 2023 KAVIN S. This is an open access article distributed under the Creative Commons Attribution License.
