DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer a...
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MDPI AG
2021-04-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/13/9/1642 |
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author | Peng Wang Yanxiong Niu Rui Xiong Fu Ma Chunxi Zhang |
author_facet | Peng Wang Yanxiong Niu Rui Xiong Fu Ma Chunxi Zhang |
author_sort | Peng Wang |
collection | DOAJ |
description | Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images. |
first_indexed | 2024-03-10T12:04:20Z |
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id | doaj.art-f44c0f614af54fa093c8bd913a8404c5 |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T12:04:20Z |
publishDate | 2021-04-01 |
publisher | MDPI AG |
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series | Remote Sensing |
spelling | doaj.art-f44c0f614af54fa093c8bd913a8404c52023-11-21T16:43:51ZengMDPI AGRemote Sensing2072-42922021-04-01139164210.3390/rs13091642DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing ImagesPeng Wang0Yanxiong Niu1Rui Xiong2Fu Ma3Chunxi Zhang4School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaRemote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images.https://www.mdpi.com/2072-4292/13/9/1642deep learningobject detectionremote sensing imagescenter-point network |
spellingShingle | Peng Wang Yanxiong Niu Rui Xiong Fu Ma Chunxi Zhang DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images Remote Sensing deep learning object detection remote sensing images center-point network |
title | DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images |
title_full | DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images |
title_fullStr | DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images |
title_full_unstemmed | DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images |
title_short | DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images |
title_sort | dganet dynamic gradient adjustment anchor free object detection in optical remote sensing images |
topic | deep learning object detection remote sensing images center-point network |
url | https://www.mdpi.com/2072-4292/13/9/1642 |
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