RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image

By Jamyoung Koo, Junghoon Seo, Seunghyun Jeon, Jeongyeol Choe, Taegyun Jeon. 2018

29 citations recorded by the source corpus.

In this paper, we propose a rotated bounding box based convolutional neural network (RBox-CNN) for arbitrary-oriented ship detection. RBox-CNN is an end-to-end model based on Faster R-CNN. The region proposal network generates proposals as the rotated bounding box, and then the rotation region-of-interest (RRoI) pooling layer is applied to extract region features corresponding the proposals. In addition, the diagonal region-of-interest (DRoI) pooling layer is applied simultaneously to extract context features and alleviate the problem of misalignment in RRoI pooling layer. To stably predict locations with the angle, we apply the regression of distance's projection in width/height. Experiments on HRSC2016 show that our model achieves state-of-the-art detection accuracy on ship detection. Furthermore, RBox-CNN achieves a significant improvement on DOTA for oriented general object detection in remote sensing images.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

  1. Philippe Giguère
  2. François Pomerleau

In Computer Vision and Pattern Recognition

  1. Jean-Michel Fortin
  2. Vincent Grondin
  3. Olivier Gamache
  4. Philippe Giguère
  5. François Pomerleau

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