AI for Maritime Security: Comparative Evaluation of Convolutional Neural Network and Vision Transformer Architectures for Maritime Object Detection
This study evaluates six deep learning architectures for maritime object detection across diverse weather conditions, demonstrating that the Vision Transformer (ViT) model outperforms CNN-based alternatives by achieving 100% accuracy, the lowest error rates, and the fastest processing speed, thereby highlighting its potential for enhancing AI-driven maritime security and surveillance.