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Ship detection has been playing a significant role in the field of remotesensing for a long time but it is still full of challenges. The mainlimitations of traditional ship detection methods usually lie in the complexityof application scenarios, the difficulty of intensive object detection and theredundancy of detection region. In order to solve such problems above, wepropose 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 isaimed 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 meansof dense connections, through which enhances the feature propagation andencourages the feature reuse. Additionally, in the case of ship rotation anddense arrangement, we design a rotation anchor strategy to predict the minimumcircumscribed rectangle of the object so as to reduce the redundant detectionregion and improve the recall. Furthermore, we also propose multi-scale ROIAlign for the purpose of maintaining the completeness of semantic and spatialinformation. Experiments based on remote sensing images from Google Earth forship detection show that our detection method based on R-DFPN representationhas a state-of-the-art performance.

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks