# ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations Source: [ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:57:51.173Z Authors: Bogdan Iancu, Valentin Soloviev, Luca Zelioli, Johan Lilius Published: 2021-02-11T07:05:33.000Z ## Abstract Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we collected a dataset of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and subsequently, we used the CSRT \[1\] tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN \[2\], R-FCN \[3\], SSD \[4\] and EfficientDet \[5\]). The algorithms were previously trained on the Microsoft COCO dataset. We compare their accuracy based on feature extractor and object size. Our experiments show that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()