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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....

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Autores principales: Wang, YunYan, Wu, Huaxuan, Shuai, Luo, Peng, Chen, Yang, Zhiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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%.
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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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