Marine vessel detection dataset and benchmark for unmanned surface vehicles

Abstract

Deep learning-based visual perception of an unmanned surface vehicle (USV) essentially benefits from informative images. It is unfortunately short of available datasets of marine vessel targets for visual perception system of USVs. In this paper, a marine vessel detection dataset, termed MVDD13, is exclusively established by deploying 35,474 images which are accurately annotated to be 13-category vessels. The proposed MVDD13 is equipped with benchmark features by sufficiently taking into account the reality of category proportion, image diversity, sample independence, and background confusion, etc., thereby facilitating rather deep information for training and testing a robust detector. To evaluate benchmark quality, the state-of-the-art deep learning detectors conducting on the MVDD13 show that individual advantages can be consistently reflected. Furthermore, by cross-dataset training/testing, intensive comparisons on general COCO and specific SeaShips datasets reveal that the MVDD13 can significantly fertilize deep learning-based recognition performance in terms of mAP@.5, mAP@.75 and mAP@[.5:.95]. All images and annotations can be downloaded via https://github.com/yyuanwang1010/MVDD13.