Disaster zone human and animal detection using sonar

Abstract

The research introduces a novel approach to animal and human detection using sonar technology in fields such as disaster management, and under water exploration. Unlike traditional visual methods, sonar systems emit soundwaves to analyze echoes, providing unique advantages in challenging environments. The proposed method involves collecting raw sonar data, followed by preprocessing techniques for noise reduction, signal normalization, and feature extraction. Sonar’s ability to penetrate various media, including water and dense fog, makes it valuable for detecting animals and humans in low-visibility conditions. Additionally, sonar operates effectively in both day and night settings, unaffected by lighting conditions. The proposed detection system will undergo comprehensive experiments using representative datasets and real-world scenarios. Performance metrics, such as detection accuracy, precision, recall, and computational efficiency, will be analyzed and compared with existing approaches. The study showcases the effectiveness and viability of employing sonar technology for animal and human detection tasks, highlighting its unique capabilities in challenging environments.