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

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.

Automated summary

The availability of domain-specific datasets is crucial in object detection, yet there is a limited number of studies on maritime vessel detection. The authors collected a dataset of maritime vessel images, annotated them accurately, and evaluated four prevalent object detection algorithms. The experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the others in most cases, except in the large object category where EfficientDet excels.