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

: Iancu Bogdan, Soloviev Valentin, Zelioli Luca, Lilius Johan

Publisher: MDPI

: 2021

Remote Sensing

: REMOTE SENSING

: REMOTE SENS-BASEL

: ARTN 988

: 13

: 5

: 17

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

: https://www.mdpi.com/2072-4292/13/5/988

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.

autonomous marine navigation, convolutional neural network, deep learning, maritime vessel dataset, Object detection, ship detection