Lightweight visual localization algorithm for UAVs

The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D position...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 6069 - 14
Hlavní autoři:	Wang, Yuhang, Feng, Xuefeng, Li, Feng, Xian, Qinglong, Jia, Zhen-Hong, Du, Zongdong, Liu, Chang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 19.02.2025 Nature Publishing Group Nature Portfolio
Témata:	639/705 639/705/117 Algorithms Deep learning Drones Humanities and Social Sciences Lightweight Load distribution Localization multidisciplinary Science Science (multidisciplinary) Vision-based positioning Visual discrimination learning Deep learning Vision-based positioning Lightweight
ISSN:	2045-2322, 2045-2322
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Abstract	The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D positioning for drones. To enhance model performance, several optimizations are proposed. Firstly, to reduce the complexity of the detection head module, GhostConv is introduced into the detection head module, constructing the GDetect detection head module. Secondly, to address the issues of imbalanced sample difficulty and uneven pixel quality in our custom dataset that result in suboptimal detection performance, Wise-IoU is introduced as the model’s bounding box regression loss function. Lastly, based on the characteristics of the drone aerial dataset samples, modifications are made to the YOLOv8n network structure to reduce redundant feature maps, resulting in the creation of the TrimYOLO network structure. Experimental results demonstrate that the Lightv8nPnP algorithm reduces the number of parameters and computational load compared to benchmark algorithms, achieves a detection rate of 186 frames per second, and maintains a positioning error of less than 5.5 centimeters across the X, Y, and Z axes in three-dimensional space.
AbstractList	Abstract The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D positioning for drones. To enhance model performance, several optimizations are proposed. Firstly, to reduce the complexity of the detection head module, GhostConv is introduced into the detection head module, constructing the GDetect detection head module. Secondly, to address the issues of imbalanced sample difficulty and uneven pixel quality in our custom dataset that result in suboptimal detection performance, Wise-IoU is introduced as the model’s bounding box regression loss function. Lastly, based on the characteristics of the drone aerial dataset samples, modifications are made to the YOLOv8n network structure to reduce redundant feature maps, resulting in the creation of the TrimYOLO network structure. Experimental results demonstrate that the Lightv8nPnP algorithm reduces the number of parameters and computational load compared to benchmark algorithms, achieves a detection rate of 186 frames per second, and maintains a positioning error of less than 5.5 centimeters across the X, Y, and Z axes in three-dimensional space. The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D positioning for drones. To enhance model performance, several optimizations are proposed. Firstly, to reduce the complexity of the detection head module, GhostConv is introduced into the detection head module, constructing the GDetect detection head module. Secondly, to address the issues of imbalanced sample difficulty and uneven pixel quality in our custom dataset that result in suboptimal detection performance, Wise-IoU is introduced as the model’s bounding box regression loss function. Lastly, based on the characteristics of the drone aerial dataset samples, modifications are made to the YOLOv8n network structure to reduce redundant feature maps, resulting in the creation of the TrimYOLO network structure. Experimental results demonstrate that the Lightv8nPnP algorithm reduces the number of parameters and computational load compared to benchmark algorithms, achieves a detection rate of 186 frames per second, and maintains a positioning error of less than 5.5 centimeters across the X, Y, and Z axes in three-dimensional space. The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D positioning for drones. To enhance model performance, several optimizations are proposed. Firstly, to reduce the complexity of the detection head module, GhostConv is introduced into the detection head module, constructing the GDetect detection head module. Secondly, to address the issues of imbalanced sample difficulty and uneven pixel quality in our custom dataset that result in suboptimal detection performance, Wise-IoU is introduced as the model's bounding box regression loss function. Lastly, based on the characteristics of the drone aerial dataset samples, modifications are made to the YOLOv8n network structure to reduce redundant feature maps, resulting in the creation of the TrimYOLO network structure. Experimental results demonstrate that the Lightv8nPnP algorithm reduces the number of parameters and computational load compared to benchmark algorithms, achieves a detection rate of 186 frames per second, and maintains a positioning error of less than 5.5 centimeters across the X, Y, and Z axes in three-dimensional space.The Lightv8nPnP lightweight visual positioning algorithm model has been introduced to make deep learning-based drone visual positioning algorithms more lightweight. The core objective of this research is to develop an efficient visual positioning algorithm model that can achieve accurate 3D positioning for drones. To enhance model performance, several optimizations are proposed. Firstly, to reduce the complexity of the detection head module, GhostConv is introduced into the detection head module, constructing the GDetect detection head module. Secondly, to address the issues of imbalanced sample difficulty and uneven pixel quality in our custom dataset that result in suboptimal detection performance, Wise-IoU is introduced as the model's bounding box regression loss function. Lastly, based on the characteristics of the drone aerial dataset samples, modifications are made to the YOLOv8n network structure to reduce redundant feature maps, resulting in the creation of the TrimYOLO network structure. Experimental results demonstrate that the Lightv8nPnP algorithm reduces the number of parameters and computational load compared to benchmark algorithms, achieves a detection rate of 186 frames per second, and maintains a positioning error of less than 5.5 centimeters across the X, Y, and Z axes in three-dimensional space.
ArticleNumber	6069
Author	Xian, Qinglong Jia, Zhen-Hong Du, Zongdong Li, Feng Feng, Xuefeng Wang, Yuhang Liu, Chang
Author_xml	– sequence: 1 givenname: Yuhang surname: Wang fullname: Wang, Yuhang organization: College of Computer Science and Technology, Xinjiang University, Xinjiang University Signal Detection and Processing Autonomous Region Key Laboratory – sequence: 2 givenname: Xuefeng surname: Feng fullname: Feng, Xuefeng organization: Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing – sequence: 3 givenname: Feng surname: Li fullname: Li, Feng organization: Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing – sequence: 4 givenname: Qinglong surname: Xian fullname: Xian, Qinglong organization: Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing – sequence: 5 givenname: Zhen-Hong surname: Jia fullname: Jia, Zhen-Hong email: jzhh@xju.edu.cn organization: College of Computer Science and Technology, Xinjiang University, Xinjiang University Signal Detection and Processing Autonomous Region Key Laboratory – sequence: 6 givenname: Zongdong surname: Du fullname: Du, Zongdong organization: College of Computer Science and Technology, Xinjiang University, Xinjiang University Signal Detection and Processing Autonomous Region Key Laboratory – sequence: 7 givenname: Chang surname: Liu fullname: Liu, Chang organization: College of Computer Science and Technology, Xinjiang University, Xinjiang University Signal Detection and Processing Autonomous Region Key Laboratory
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SubjectTerms	639/705 639/705/117 Algorithms Deep learning Drones Humanities and Social Sciences Lightweight Load distribution Localization multidisciplinary Science Science (multidisciplinary) Vision-based positioning Visual discrimination learning
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Title	Lightweight visual localization algorithm for UAVs
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