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Detection of plane in remote sensing images using super-resolution
The object detection of remote sensing image often has low accuracy and high missed or false detection rate due to the large number of small objects, instance level noise and cloud occlusion. In this paper, a new object detection model based on SRGAN and YOLOV3 is proposed, which is called SR-YOLO....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022876/ https://www.ncbi.nlm.nih.gov/pubmed/35446858 http://dx.doi.org/10.1371/journal.pone.0265503 |
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author | Wang, YunYan Wu, Huaxuan Shuai, Luo Peng, Chen Yang, Zhiwei |
author_facet | Wang, YunYan Wu, Huaxuan Shuai, Luo Peng, Chen Yang, Zhiwei |
author_sort | Wang, YunYan |
collection | PubMed |
description | The object detection of remote sensing image often has low accuracy and high missed or false detection rate due to the large number of small objects, instance level noise and cloud occlusion. In this paper, a new object detection model based on SRGAN and YOLOV3 is proposed, which is called SR-YOLO. It solves the problems of SRGAN network sensitivity to hyper-parameters and modal collapse. Meanwhile, The FPN network in YOLOv3 is replaced by PANet, shortened the distance between the lowest and the highest layers, and the SR-YOLO model has strong robustness and high detection ability by using the enhanced path to enrich the characteristics of each layer. The experimental results on the UCAS-High Resolution Aerial Object Detection Dataset showed SR-YOLO has achieved excellent performance. Compared with YOLOv3, the average precision (AP) of SR-YOLO increased from 92.35% to 96.13%, the log-average miss rate (MR(-2)) decreased from 22% to 14%, and the Recall rate increased from 91.36% to 95.12%. |
format | Online Article Text |
id | pubmed-9022876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90228762022-04-22 Detection of plane in remote sensing images using super-resolution Wang, YunYan Wu, Huaxuan Shuai, Luo Peng, Chen Yang, Zhiwei PLoS One Research Article The object detection of remote sensing image often has low accuracy and high missed or false detection rate due to the large number of small objects, instance level noise and cloud occlusion. In this paper, a new object detection model based on SRGAN and YOLOV3 is proposed, which is called SR-YOLO. It solves the problems of SRGAN network sensitivity to hyper-parameters and modal collapse. Meanwhile, The FPN network in YOLOv3 is replaced by PANet, shortened the distance between the lowest and the highest layers, and the SR-YOLO model has strong robustness and high detection ability by using the enhanced path to enrich the characteristics of each layer. The experimental results on the UCAS-High Resolution Aerial Object Detection Dataset showed SR-YOLO has achieved excellent performance. Compared with YOLOv3, the average precision (AP) of SR-YOLO increased from 92.35% to 96.13%, the log-average miss rate (MR(-2)) decreased from 22% to 14%, and the Recall rate increased from 91.36% to 95.12%. Public Library of Science 2022-04-21 /pmc/articles/PMC9022876/ /pubmed/35446858 http://dx.doi.org/10.1371/journal.pone.0265503 Text en © 2022 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, YunYan Wu, Huaxuan Shuai, Luo Peng, Chen Yang, Zhiwei Detection of plane in remote sensing images using super-resolution |
title | Detection of plane in remote sensing images using super-resolution |
title_full | Detection of plane in remote sensing images using super-resolution |
title_fullStr | Detection of plane in remote sensing images using super-resolution |
title_full_unstemmed | Detection of plane in remote sensing images using super-resolution |
title_short | Detection of plane in remote sensing images using super-resolution |
title_sort | detection of plane in remote sensing images using super-resolution |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022876/ https://www.ncbi.nlm.nih.gov/pubmed/35446858 http://dx.doi.org/10.1371/journal.pone.0265503 |
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