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

Autor:	He, Shitian, Zou, Huanxin, Wang, Yingqian, Li, Runlin, Cheng, Fei
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to IGARSS 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.09699 Zobrazit plný text záznamu View this record from Arxiv