Ship target detection in high resolution remote sensing images based on characteristic pyramid model

  • ZHOU Hui ,
  • YAN Feng-long ,
  • CHU Na ,
  • CHEN Peng
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  •  (1.School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China;2.Navigation College, Dalian Maritime University,Dalian 116026, China)

Received date: 2019-05-14

  Revised date: 2019-06-19

  Online published: 2019-06-19

Abstract

Taking ships as the study object, the key technologies of multi-scale and multi-target detection in high resolution remote sensing images were studied to solve the problems of multi-scale and multi-target recognition and low accuracy of fine-grained classification. In the aspect of target location, the feature pyramid depth network was used to locate the multi-target area, and a feature pyramid with semantic information on all scales was created to effectively solve the low accuracy problem of multi-scale and multitarget data location. In target recognition, the shared CNN network was used to reconstruct the input image, optimize the multi task loss function to extract the structural features of fine-grained classified targets, and improve the accuracy of subdivision target recognition. The comparison with the three target detection algorithms of GoogLeNet、Faster R-CNN and Yolo shows that multi-target and multi-scale fine grained ship objects can be effectively detected by using feature pyramids and reconstructed input images. The missed detection rate is 1.5%, and the average accuracy of fine-grained classification and recognition is 92.67%.

Cite this article

ZHOU Hui , YAN Feng-long , CHU Na , CHEN Peng . Ship target detection in high resolution remote sensing images based on characteristic pyramid model[J]. Journal of Dalian Maritime University, 2019 , 45(4) : 131 -138 . DOI: 10.16411/j.cnki.issn1006-7736.2019.04.018

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