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Article

ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations

Faculty of Science and Engineering, Åbo Akademi University, 20500 Åbo, Finland
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Author to whom correspondence should be addressed.
Current address: Åbo Akademi, Agora, Informationsteknologi, Vattenborgsvägen 3, 20500 Åbo, Finland.
Academic Editor: Pedro Melo-Pinto
Remote Sens. 2021, 13(5), 988; https://doi.org/10.3390/rs13050988
Received: 4 February 2021 / Revised: 27 February 2021 / Accepted: 1 March 2021 / Published: 5 March 2021
Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter. View Full-Text
Keywords: maritime vessel dataset; ship detection; object detection; convolutional neural network; deep learning; autonomous marine navigation maritime vessel dataset; ship detection; object detection; convolutional neural network; deep learning; autonomous marine navigation
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MDPI and ACS Style

Iancu, B.; Soloviev, V.; Zelioli, L.; Lilius, J. ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations. Remote Sens. 2021, 13, 988. https://doi.org/10.3390/rs13050988

AMA Style

Iancu B, Soloviev V, Zelioli L, Lilius J. ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations. Remote Sensing. 2021; 13(5):988. https://doi.org/10.3390/rs13050988

Chicago/Turabian Style

Iancu, Bogdan, Valentin Soloviev, Luca Zelioli, and Johan Lilius. 2021. "ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations" Remote Sensing 13, no. 5: 988. https://doi.org/10.3390/rs13050988

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