Constraint Loss for Rotated Object Detection in Remote Sensing Images

Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection met...

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Autores principales:	Luyang Zhang, Haitao Wang, Lingfeng Wang, Chunhong Pan, Qiang Liu, Xinyao Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rotated object detection remote sensing image loss functions fast convergence Science Q
Acceso en línea:	https://doaj.org/article/acd2c41bbb26464b8615e0d8c1436ce8
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spelling	oai:doaj.org-article:acd2c41bbb26464b8615e0d8c1436ce82021-11-11T18:53:06ZConstraint Loss for Rotated Object Detection in Remote Sensing Images10.3390/rs132142912072-4292https://doaj.org/article/acd2c41bbb26464b8615e0d8c1436ce82021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4291https://doaj.org/toc/2072-4292Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss is used as the regression loss function. However, we argue that smooth <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.Luyang ZhangHaitao WangLingfeng WangChunhong PanQiang LiuXinyao WangMDPI AGarticlerotated object detectionremote sensing imageloss functionsfast convergenceScienceQENRemote Sensing, Vol 13, Iss 4291, p 4291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rotated object detection remote sensing image loss functions fast convergence Science Q
spellingShingle	rotated object detection remote sensing image loss functions fast convergence Science Q Luyang Zhang Haitao Wang Lingfeng Wang Chunhong Pan Qiang Liu Xinyao Wang Constraint Loss for Rotated Object Detection in Remote Sensing Images
description	Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss is used as the regression loss function. However, we argue that smooth <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.
format	article
author	Luyang Zhang Haitao Wang Lingfeng Wang Chunhong Pan Qiang Liu Xinyao Wang
author_facet	Luyang Zhang Haitao Wang Lingfeng Wang Chunhong Pan Qiang Liu Xinyao Wang
author_sort	Luyang Zhang
title	Constraint Loss for Rotated Object Detection in Remote Sensing Images
title_short	Constraint Loss for Rotated Object Detection in Remote Sensing Images
title_full	Constraint Loss for Rotated Object Detection in Remote Sensing Images
title_fullStr	Constraint Loss for Rotated Object Detection in Remote Sensing Images
title_full_unstemmed	Constraint Loss for Rotated Object Detection in Remote Sensing Images
title_sort	constraint loss for rotated object detection in remote sensing images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/acd2c41bbb26464b8615e0d8c1436ce8
work_keys_str_mv	AT luyangzhang constraintlossforrotatedobjectdetectioninremotesensingimages AT haitaowang constraintlossforrotatedobjectdetectioninremotesensingimages AT lingfengwang constraintlossforrotatedobjectdetectioninremotesensingimages AT chunhongpan constraintlossforrotatedobjectdetectioninremotesensingimages AT qiangliu constraintlossforrotatedobjectdetectioninremotesensingimages AT xinyaowang constraintlossforrotatedobjectdetectioninremotesensingimages
_version_	1718431718738755584

Constraint Loss for Rotated Object Detection in Remote Sensing Images

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