# ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation

arXiv: https://arxiv.org/abs/2103.09699 · HTML: https://mlsanity.com/paper/2103.09699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network](https://mlsanity.com/paper/2003.09085/) (2020)
- [Symmetric Parallax Attention for Stereo Image Super-Resolution](https://mlsanity.com/paper/2011.03802/) (2020)
- [Light Field Image Super-Resolution Using Deformable Convolution](https://mlsanity.com/paper/2007.03535/) (2020)

## Built on by (most cited first, 1 of 1)

- [DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images](https://mlsanity.com/paper/2110.01025/) (2021)

## Abstract (arXiv)

High-resolution remote sensing images can provide abundant appearance information for ship detection. Although several existing methods use image super-resolution (SR) approaches to improve the detection performance, they consider image SR and ship detection as two separate processes and overlook the internal coherence between these two correlated tasks. In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet. In our method, we not only feed the super-resolved images to the detector but also integrate the intermediate features of the SR network with those of the detection network. In this way, the informative feature representation extracted by the SR network can be fully used for ship detection. Experimental results on the HRSC dataset validate the effectiveness of our method. Our ShipSRDet can recover the missing details from the input image and achieves promising ship detection performance.