Exploring AI Techniques in the Identification and Control of Marine Vehicles

Abstract—The rapid advancement of artificial intelligence (AI) offers new avenues for enhancing the identification, control, and autonomous operation of marine vehicles. This study investigates the application of AI techniques in maritime environments, focusing on object detection, navigation, and autonomous control to support safe and efficient operations in various marine conditions. Key objectives include evaluating machine learning models for identifying and tracking marine vehicles and the development of intelligent control algorithms that can adapt to dynamic oceanic settings. Methods involved training convolutional neural networks (CNNs) on datasets of marine images for object identification and using reinforcement learning (RL) algorithms to optimize the control systems of autonomous marine vehicles. Results demonstrate that CNN-based models achieve high accuracy in vehicle identification, even under challenging visual conditions such as low lighting or occlusion. At the same time, RL-driven control systems adapt effectively to complex, fluctuating marine environments. Simulated and real-world testing indicated that these AI techniques improve vessel maneuverability and response times, leading to more efficient and safer operations. In conclusion, this study highlights the potential of AI to revolutionize marine vehicle identification and control, with implications for enhanced security, efficiency, and sustainability in maritime operations. It is advisable to conduct additional research to improve these models, enabling their application across a wider range of marine environments.

Keywords:

Keywords—Artificial Intelligence , Machine Learning , Autonomous Navigation , Marine Vehicles , AI Techniques