Arbitrary Direction Ship Detection in Remote-Sensing Images Based on Multitask Learning and Multiregion Feature Fusion

摘要

Ship detection in remote sensing plays an important role in civil and military fields. Owing to the complex background and uncertain direction, ship detection is full of challenge by using the commonly used object-detection methods. In this article, a new framework for detecting the arbitrary direction ships is proposed based on the improvement in the Faster region-based convolutional network (R-CNN), in which the shape of the bounding box is described by three sides, namely, vertical side, horizontal side, and short side, respectively. The inclination of the ship is obtained by calculating the arc-tangent value of the vertical side to the horizontal side. First, the better performing ResNet-101 is adopted to extract features over an entire image, which are shared by the region proposal network (RPN) and the head network. Then, the multidirection proposal regions that may contain ships are generated by the RPN. Next, the global and local features of the proposal regions are combined as the whole features of the regions by a multiregion feature-fusion (MFF) module, which can provide more detailed information of the regions. Finally, the head network uses the whole features of the proposal regions for bounding-box recognition through multitask learning, including classification, regression, and incline direction prediction (left or right). The proposed method is tested and compared with other state-of-the-art ship-detection methods on two open remote-sensing data sets and some large-scale and real images. The experimental results validate that the proposed approach has achieved better performance.