Detect Larger at Once: Large-Area Remote-Sensing Image Arbitrary-Oriented Ship Detection

Ship detection is one of the main problems of satellite image analysis. Since ships are scattered on the sea and major ports, large-area remote-sensing images need to be processed in order to realize the detection of ships. In addition, since the satellite is a top-down view, the ship with aspect ratios cannot be covered in complex backgrounds by a horizontal bounding box very well and need a rotating bounding box to achieve this task. Although considerable progress has been made in object detection techniques, there are still challenges for fast detection of ships in large-area remote-sensing images. In this letter, an arbitrary-oriented detector for large-area remote-sensing images is proposed to quickly locate ship positions. A new feature extraction network DCNDarknet25 based on you only look once (YOLO) is designed by reducing paraments and adding deformable convolution (DCN) to improve the speed and accuracy. And the rotation detection capability without angle regression is added to the YOLO detection algorithm for the first time. Finally, thanks to the advantages of our fully convolutional lightweight network, a method for detecting large-area remote-sensing images at once is proposed. In the public dataset HRSC2016 and our own large-area remote-sensing (LARS) image dataset, it has achieved very good accuracy and several times the speed of other algorithms.

Automated summary

Ship detection is a major problem in satellite image analysis, especially for fast detection of ships in large-area remote-sensing images. This letter proposes an arbitrary-oriented detector based on YOLO, which can quickly locate ship positions. By reducing parameters, adding deformable convolution, and integrating rotation detection capability without angle regression, the proposed method achieves excellent accuracy and speed.