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.
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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
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Revised: 27 February 2021
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Accepted: 1 March 2021
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Published: 5 March 2021
(This article belongs to the Special Issue Application of Multi-Sensor Fusion Technology in Target Detection and Recognition)
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.
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Keywords:
maritime vessel dataset; ship detection; object detection; convolutional neural network; deep learning; autonomous marine navigation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 StyleIancu, 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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