  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.
