A satellite image target detection model based on an improved single-stage target detection network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at the problem that it is difficult to detect small targets in satellite images, this paper proposes an improved method based on deep convolutional neural network YOLO V3. Firstly, the network structure of the original YOLO V3 was modified, and the target detection layer of three scales was reset. Then, during the detection process, since the test image is too large, the image is cut through the sliding window and then detected. During the experiment, the original YOLO V3 network and the improved network were used to train and test on the dataset. The experimental results show that the improved network improves the detection accuracy by 1.79% and the recall rate by 4.55%, the AP increased by 4.34%.

Original languageEnglish
Title of host publicationProceedings - 2019 Chinese Automation Congress, CAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4931-4936
Number of pages6
ISBN (Electronic)9781728140940
DOIs
StatePublished - Nov 2019
Event2019 Chinese Automation Congress, CAC 2019 - Hangzhou, China
Duration: 22 Nov 201924 Nov 2019

Publication series

NameProceedings - 2019 Chinese Automation Congress, CAC 2019

Conference

Conference2019 Chinese Automation Congress, CAC 2019
Country/TerritoryChina
CityHangzhou
Period22/11/1924/11/19

Keywords

  • YOLO V3
  • deep learning
  • satellite image
  • sliding window
  • small target detection

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