Comparative study of feature extraction approaches for maritime vessel identification in CBIR Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.5753/eniac.2024.245117
· OA: W4408691466
Maritime surveillance and monitoring systems are crucial in coastal security and resource management. Vessel recognition and identification are key tasks. However, visual inspection is a costly and labour-intensive process. This study compares methods for an automated approach for vessel identification using digital image processing. The performance of classical and Machine Learning-based feature extraction methods is evaluated and compared using a maritime vessel dataset to verify their ability to identify different vessels. The results show that BEiT-v2 achieves the highest identification performance with a mean Average Precision (mAP) of 95.05%. VGG-19 offers the best balance between accuracy (second-highest mAP) and computational cost. These findings suggest that Machine Learning methods are valuable for vessel identification, with the optimal choice depending on the specific needs of the application.
