{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:18:43Z","timestamp":1775942323302,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13050988","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T05:03:15Z","timestamp":1614920595000},"page":"988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["ABOships\u2014An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations"],"prefix":"10.3390","volume":"13","author":[{"given":"Bogdan","family":"Iancu","sequence":"first","affiliation":[{"name":"Faculty of Science and Engineering, \u00c5bo Akademi University, 20500 \u00c5bo, Finland"}]},{"given":"Valentin","family":"Soloviev","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, \u00c5bo Akademi University, 20500 \u00c5bo, Finland"}]},{"given":"Luca","family":"Zelioli","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, \u00c5bo Akademi University, 20500 \u00c5bo, Finland"}]},{"given":"Johan","family":"Lilius","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, \u00c5bo Akademi University, 20500 \u00c5bo, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TCSVT.2019.2897980","article-title":"Saliency-aware convolution neural network for ship detection in surveillance video","volume":"30","author":"Shao","year":"2019","journal-title":"IEEE Trans. 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