YOLOv8 Neural Network Application for Noncollaborative Vessel Detection Using Sentinel-1 SAR Data: A Case Study

Noncollaborative vessels are usually involved in illegal activities and actively monitoring these vessels is one of the most challenging task. This study introduces a methodology that combines automatic identification system (AIS) data and SAR images into a YOLOv8+ slicing-aided hyper inference (SAH...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2025, Vol.22, p.1-5
Main Authors: Caricchio, Camilla, Mendonca, Luis Felipe, Lentini, Carlos A. D., Lima, Andre T. C., Silva, David O., Meirelles e Goes, Pedro H.
Format: Article
Language:English
Subjects:
Accuracy
and unregulated (IUU) fishing
Artificial intelligence
Convolutional neural networks (CNNs)
dark ships
illegal
machine learning
Marine vehicles
Neural networks
Radar polarimetry
Recall
Rivers
Satellite images
Satellites
Spaceborne radar
Statistical models
Synthetic aperture radar
Training
unreported
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Summary:Noncollaborative vessels are usually involved in illegal activities and actively monitoring these vessels is one of the most challenging task. This study introduces a methodology that combines automatic identification system (AIS) data and SAR images into a YOLOv8+ slicing-aided hyper inference (SAHI)-based approach, as a decision aid tool for noncooperative vessel detection, to improve maritime domain awareness. It was used 1958 augmented images to custom train the YOLOv8 neural network. For the study case, 16 Sentinel high-resolution ground range detected (GRDH)- interferometric wide (IW) SAR images were used. During the training, the custom model achieved excellent performance with satisfactory statistical results (mAP@.5: 94.3%, precision: 92.5%, and recall: 91.9%), especially when compared to similar previous studies. The model was able to correctly distinguish between vessels and nonvessel features, such as islands, rivers, or coastlines. In the study case, the false negative (FN) detection rate was 95.4%, similar to mAp@0.5 results found at the training and validation step and the Recall was 95.6%, considered excellent results. The recall improvement in the study case shows that the model's performance in real-world scenarios is better than initially expected for application in noncollaborative vessel detection systems. The model presented showed very promising results for the operational detection of darkships using, simultaneous, SAR images and AIS data.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3508462