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Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinc...

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Detalles Bibliográficos
Autores principales: Sun, Yu, Zhi, Xiyang, Han, Haowen, Jiang, Shikai, Shi, Tianjun, Gong, Jinnan, Zhang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347213/
https://www.ncbi.nlm.nih.gov/pubmed/37447887
http://dx.doi.org/10.3390/s23136037
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author Sun, Yu
Zhi, Xiyang
Han, Haowen
Jiang, Shikai
Shi, Tianjun
Gong, Jinnan
Zhang, Wei
author_facet Sun, Yu
Zhi, Xiyang
Han, Haowen
Jiang, Shikai
Shi, Tianjun
Gong, Jinnan
Zhang, Wei
author_sort Sun, Yu
collection PubMed
description The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network’s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.
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spelling pubmed-103472132023-07-15 Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow Sun, Yu Zhi, Xiyang Han, Haowen Jiang, Shikai Shi, Tianjun Gong, Jinnan Zhang, Wei Sensors (Basel) Article The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network’s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving. MDPI 2023-06-29 /pmc/articles/PMC10347213/ /pubmed/37447887 http://dx.doi.org/10.3390/s23136037 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Yu
Zhi, Xiyang
Han, Haowen
Jiang, Shikai
Shi, Tianjun
Gong, Jinnan
Zhang, Wei
Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title_full Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title_fullStr Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title_full_unstemmed Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title_short Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
title_sort enhancing uav detection in surveillance camera videos through spatiotemporal information and optical flow
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347213/
https://www.ncbi.nlm.nih.gov/pubmed/37447887
http://dx.doi.org/10.3390/s23136037
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