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Small Ship Detection on Optical Satellite Imagery with YOLO and YOLT

  • Wilder Nina
  • William Condori
  • Vicente Machaca
  • Juan Villegas
  • Eveling CastroEmail author
Conference paper
  • 13 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)

Abstract

Actually, the use of deep learning in object detection gives good results, but this performance decreases when there are small objects in the image. In this work, is presented a comparison between the last version of You Only Look Once (YOLO) and You Only Look Twice (YOLT) on the problem of detecting small objects (ships) on optical satellite imagery. Two datasets were used: High-Resolution Ship Collection (HRSC) and Mini Ship Data Set (MSDS), the last one was built by us. The mean object’s width for HRSC and MSDS are 150 and 50 pixels, respectively. The results showed that YOLT is good only for small objects with 76,06% of Average Precision (AP), meanwhile, YOLO reached 69,80% in the MSDS dataset. Moreover, in the case of the HRSC dataset where have objects of different sizes, YOLT obtained a 40% of AP against 75% of YOLO.

Keywords

YOLO YOLT Small objects Object detection Ship detection Satellite Imagery 

Notes

Acknowledgment

This research was supported from Universidad Nacional de San Agustín de Arequipa Contract: IBA-0032-2017-UNSA, like part of the project “Detection of industrial fishing vessels within 5 miles of the Arequipa Region using high performance computing and satellite images”. Thanks to the CiTeSoft Contract: EC-0003-2017-UNSA for the equipment and the resources bring to the project.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wilder Nina
    • 1
  • William Condori
    • 1
  • Vicente Machaca
    • 2
  • Juan Villegas
    • 1
  • Eveling Castro
    • 1
    Email author
  1. 1.Universidad Nacional de San Agustín de ArequipaArequipaPeru
  2. 2.Universidad La Salle de ArequipaArequipaPeru

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