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

Bogdan Iancu*, Valentin Soloviev, Luca Zelioli, Johan Lilius

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
246 Downloads (Pure)


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.
Original languageEnglish
Article number988
Pages (from-to)1-17
Number of pages17
JournalRemote Sensing
Issue number5
Publication statusPublished - 5 Feb 2021
MoE publication typeA1 Journal article-refereed


  • maritime vessel dataset
  • ship detection
  • object detection
  • convolutional neural network
  • deep learning
  • autonomous marine navigation


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