A global object-oriented dynamic network for low-altitude remote sensing object detection

With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computatio...

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Published in:Scientific reports Vol. 15; no. 1; pp. 19071 - 20
Main Authors: Tang, Daoze, Tang, Shuyun, Wang, Yalin, Guan, Shaoyun, Jin, Yining
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 30.05.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/987
639/705/117
639/705/258
639/705/794
Algorithms
Altitude
Computer applications
Deep learning
Drone
Humanities and Social Sciences
Image processing
Images processing
Information processing
Low-altitude remote sensing
multidisciplinary
Real-time
Remote sensing
Science
Science (multidisciplinary)
Deep learning
Images processing
Drone
Low-altitude remote sensing
Real-time
ISSN:2045-2322, 2045-2322
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Abstract With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm’s scalability and practical applicability. Visualization methods further highlight the algorithm’s performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.
AbstractList With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm’s scalability and practical applicability. Visualization methods further highlight the algorithm’s performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.
With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm's scalability and practical applicability. Visualization methods further highlight the algorithm's performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm's scalability and practical applicability. Visualization methods further highlight the algorithm's performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.
Abstract With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm’s scalability and practical applicability. Visualization methods further highlight the algorithm’s performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.
ArticleNumber 19071
Author Tang, Daoze
Tang, Shuyun
Wang, Yalin
Guan, Shaoyun
Jin, Yining
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Issue 1
Keywords Deep learning
Images processing
Drone
Low-altitude remote sensing
Real-time
Language English
License 2025. The Author(s).
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Snippet With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban...
Abstract With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time...
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SubjectTerms 639/166/987
639/705/117
639/705/258
639/705/794
Algorithms
Altitude
Computer applications
Deep learning
Drone
Humanities and Social Sciences
Image processing
Images processing
Information processing
Low-altitude remote sensing
multidisciplinary
Real-time
Remote sensing
Science
Science (multidisciplinary)
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Title A global object-oriented dynamic network for low-altitude remote sensing object detection
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