To simulate realistic data, the generated vascular vessel tree datasets were then smoothed by Gaussian blurring and noise was added.
Existing benchmark retinal vessel segmentation datasets such as the DRIVE [ 32], STARE [ 14] and REVIEW [ 33] databases do not include VIO images.
We use velocity max to represent the maximum velocity of a vessel in the dataset.
Speed frequency profiles, produced for each vessel in the NEAFC dataset using GeoCrust2.0 software [40], were provided by ICES.
To the best of our knowledge, the collected dataset, MARitime VEsseLs (MARVEL) [13, 14], is the largest-scale dataset with meta-data composed of the aforementioned attributes, suited for fine-grained visual categorization, recognition, retrieval, and verification tasks, as well as any possible future applications.
Al-Diri and others [51] reported about the REVIEW dataset for retinal vessel and the algorithm used to process the segmentation to produce vessel profiles.
As a further check the entire 2005 NEAFC dataset comprising 797 vessels was imported into ArcGIS and patterns of vessel activity, following seafloor contours were studied.
Fig. 2 Histograms of four vessel attribute values on MARVEL dataset: a draught, b length, c gross tonnage, and d summer deadweight.
It is also feasible to use the dataset for both vessel verification and identity recognition, which could be a vital part of a maritime security system, analogous to a scenario where vehicle make and model recognition is crucial for a traffic security system.
We introduced, MARVEL, a large-scale image dataset for maritime vessels, consisting of 2 million user-uploaded images and their various attributes, including vessel identity, type, category, year built, length, and tonnage, collected from a community website.
