Open AccessAlongshore Ship Detection Based On Multiscale Feature Fusion of Rotation Region Proposal Networks
Author(s) -
Haoran Dou,
Jiaxing Mao,
Zhihong Pan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012148
Subject(s) - rotation (mathematics) , bounding overwatch , context (archaeology) , feature (linguistics) , computer science , pooling , position (finance) , artificial intelligence , sensor fusion , pattern recognition (psychology) , data mining , geology , paleontology , linguistics , philosophy , finance , economics
In this paper, we propose an alongshore ship detection method based on multiscale feature fusion of rotation region proposal networks, the method provides an end-to-end ship detection framework, which is divided into three modules: multiscale feature module, rotation region proposal network module and context rotation region pooling module. The multiscale feature module integrates multiscale features, the rotation region proposal module can generate rotating bounding boxes in any direction, the context rotating region pooling module fuses the context information to pool the rotating region and complete the classification and position regression of bounding boxes. Experimental results show that our method can achieve good performance in remote sensing data sets.