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

نویسندگان

چکیده

Availability of domain-specific datasets is an essential problem in object detection. Datasets inshore and offshore maritime vessels are no exception, with a limited number studies addressing vessel detection on such datasets. For that reason, we collected dataset consisting images taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion scale variation. Vessel instances (including nine types vessels), seamarks miscellaneous floaters were precisely annotated: employed first round labelling subsequently used the CSRT tracker to trace inconsistencies relabel inadequate label instances. Moreover, evaluated out-of-the-box performance four prevalent algorithms (Faster R-CNN, R-FCN, SSD EfficientDet). The previously trained Microsoft COCO dataset. We compared their accuracy based feature extractor size. Our experiments showed Faster R-CNN Inception-Resnet v2 outperforms other algorithms, except large category where EfficientDet surpasses latter.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13050988