ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations
This provides a new dataset for maritime vessel detection, addressing a domain-specific need but is incremental as it builds on existing methods and datasets.
The authors tackled the lack of domain-specific datasets for maritime vessel detection by collecting and precisely annotating a new dataset (ABOShips) with 9 vessel types, seamarks, and floaters, and evaluated four object detection algorithms, finding Faster R-CNN with Inception-Resnet v2 generally outperforms others except for large objects where EfficientDet is better.
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.