AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection
A new study published on arXiv evaluates the effectiveness of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for maritime security applications, specifically ship detection. The research utilized a dataset of 6,468 maritime images across various weather conditions and compared six deep learning architectures. Results indicated that while lightweight models are suitable for constrained environments, the Vision Transformer achieved superior performance with 100% accuracy and the fastest processing times. AI
IMPACT Vision Transformers show promise for enhancing maritime surveillance and autonomous navigation systems.