  Ship detection has been playing a significant role in the field of remote
sensing for a long time but it is still full of challenges. The main
limitations of traditional ship detection methods usually lie in the complexity
of application scenarios, the difficulty of intensive object detection and the
redundancy of detection region. In order to solve such problems above, we
propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN)
which can effectively detect ship in different scenes including ocean and port.
Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is
aimed at solving the problem resulted from the narrow width of the ship.
Compared with previous multi-scale detectors such as Feature Pyramid Network
(FPN), DFPN builds the high-level semantic feature-maps for all scales by means
of dense connections, through which enhances the feature propagation and
encourages the feature reuse. Additionally, in the case of ship rotation and
dense arrangement, we design a rotation anchor strategy to predict the minimum
circumscribed rectangle of the object so as to reduce the redundant detection
region and improve the recall. Furthermore, we also propose multi-scale ROI
Align for the purpose of maintaining the completeness of semantic and spatial
information. Experiments based on remote sensing images from Google Earth for
ship detection show that our detection method based on R-DFPN representation
has a state-of-the-art performance.
