Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.
Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods
07.06.2023
883460 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vessel Detection With Wide Area Remote Sensing
British Library Online Contents | 1998
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