Enhancing Inland Waterway Safety and Management through Machine Learning-Based Ship Detection

Abstract

Efficient ship detection is essential for inland waterway management. Recent advances in artificial intelligence have prompted research in this field. This study introduces a real-time ship detection model utilizing computer vision and the YOLO object detection framework. The model is designed to identify and locate common inland waterway vessels, such as container ships, passenger vessels, barges, ferries, canoes, fishing boats, and sailboats. Data augmentation techniques were employed to enhance the model's ability to handle variations in ship appearance, weather, and image quality. The system achieved a mean Average Precision (mAP) of 98.4%, with precision and recall rates of 96.6% and 95.0%, respectively. These results demonstrate the model's effectiveness in practical applications. Its ability to generalize across diverse vessel types and environmental conditions suggests its potential integration into video surveillance for improved maritime safety, traffic control, and search and rescue operations.