AI-Driven Underwater Surveillance for Maritime Security and Environmental Monitoring

Abstract

The utilization of artificial intelligence (AI) is transforming underwater surveillance. It has made underwater surveillance smart, autonomous, and real-time. This chapter discusses the ways that AI technologies such as machine learning, deep learning, computer vision, and reinforcement learning are being incorporated into underwater systems to mitigate well-known challenges, particularly low visibility, high latency, and operational constraints, which made surveillance hard. By integrating technologies like autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and Internet of Things (IoT) sensor platforms with algorithms, AI had enabled accurate interpretation of sonar, acoustic, and visual data, adaptive navigation, and real-time anomaly detection. The chapter directs readers towards the principal applications such as underwater object detection, 3D mapping, environmental monitoring, and maritime security to discuss the ethical, logical, and technical issues like data privacy, explainability, and resource limitations in underwater networks. It also covers the methodology of AI in underwater surveillance and presents the comparative analysis of conventional and AI-driven approaches. With the help of case studies and real-world applications, this chapter highlights the revolution AI will bring in increasing the efficiency, precision, and scalability of underwater surveillance systems as well as towards cognitive ocean monitoring and environmentally sustainable marine management.