Machine Learning Approaches for Underwater Object Classification using Sonar Data

The need for analyzing a sonar dataset using machine learning algorithms arises from critical applications such as naval operations, marine exploration, and environmental monitoring. Accurate classification of underwater objects, including rocks and mines, is essential for maritime safety, environmental assessment, and underwater security. Manual interpretation of sonar data is time- consuming and error-prone, necessitating automated classification methods. By leveraging machine learning, we aim to develop robust models capable of efficiently identifying underwater objects, thereby advancing underwater sensing technology and supporting various marine-related endeavors. Here, we investigate the efficacy of Random Forest and Linear Regression machine learning algorithms in analyzing a sonar dataset comprising frequencies to predict whether an object is a rock or a mine. The primary objective is to compare the performance of these two algorithms in classifying underwater objects based on acoustic signals. By leveraging machine learning techniques, we aim to develop models capable of effectively discriminating between rocks and mines based on their acoustic signatures, thus addressing the challenge of underwater object identification. Therefore, the experimental setup involves preprocessing techniques to enhance feature extraction and mitigate noise, ensuring the robustness of the model and optimizing computational efficiency without compromising accuracy.