Unveiling SONAR Echoes: Using Machine Learning to Classify Rock vs Mine

This research paper explores the use of machine learning techniques to classify underwater objects as rocks or mines using SONAR echo data.Accurate detection of underwater mines is essential for maritime safety and ocean engineering, as traditional manual methods often lack efficiency and reliability.In this study, supervised machine learning models are applied to identify distinguishing patterns in SONAR signal features that separate natural rock formations from man-made mines.The dataset is pre-processed using normalization and feature standardization to improve learning performance and prediction accuracy.Classification algorithms such as Logistic Regression and Random Forest are trained and evaluated using standard performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC.The experimental results show that the implemented models achieve reliable and consistent classification outcomes.This study highlights the effectiveness of machine learning-based analysis for automated underwater object detection and demonstrates its potential in reducing human intervention.The proposed approach provides a basis for developing intelligent detection systems and can be further enhanced by incorporating advanced learning methods and real-time SONAR data.