A Lightweight Border Patrol Object Detection Network for Edge Devices

Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In...
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Autor*in:	Lei Yue [verfasserIn] Haifeng Ling [verfasserIn] Jianhu Yuan [verfasserIn] Linyuan Bai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	object detection deep learning computer vision border patrol

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 11(2022), 22, p 3828
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:22, p 3828

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics11223828

Katalog-ID:	DOAJ085591270

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spelling	10.3390/electronics11223828 doi (DE-627)DOAJ085591270 (DE-599)DOAJ9f60e7c9a48e4c57aabc5c3656f21297 DE-627 ger DE-627 rakwb eng TK7800-8360 Lei Yue verfasserin aut A Lightweight Border Patrol Object Detection Network for Edge Devices 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection. object detection deep learning computer vision border patrol Electronics Haifeng Ling verfasserin aut Jianhu Yuan verfasserin aut Linyuan Bai verfasserin aut In Electronics MDPI AG, 2013 11(2022), 22, p 3828 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:22, p 3828 https://doi.org/10.3390/electronics11223828 kostenfrei https://doaj.org/article/9f60e7c9a48e4c57aabc5c3656f21297 kostenfrei https://www.mdpi.com/2079-9292/11/22/3828 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 3828
allfields_unstemmed	10.3390/electronics11223828 doi (DE-627)DOAJ085591270 (DE-599)DOAJ9f60e7c9a48e4c57aabc5c3656f21297 DE-627 ger DE-627 rakwb eng TK7800-8360 Lei Yue verfasserin aut A Lightweight Border Patrol Object Detection Network for Edge Devices 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection. object detection deep learning computer vision border patrol Electronics Haifeng Ling verfasserin aut Jianhu Yuan verfasserin aut Linyuan Bai verfasserin aut In Electronics MDPI AG, 2013 11(2022), 22, p 3828 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:22, p 3828 https://doi.org/10.3390/electronics11223828 kostenfrei https://doaj.org/article/9f60e7c9a48e4c57aabc5c3656f21297 kostenfrei https://www.mdpi.com/2079-9292/11/22/3828 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 3828
allfieldsGer	10.3390/electronics11223828 doi (DE-627)DOAJ085591270 (DE-599)DOAJ9f60e7c9a48e4c57aabc5c3656f21297 DE-627 ger DE-627 rakwb eng TK7800-8360 Lei Yue verfasserin aut A Lightweight Border Patrol Object Detection Network for Edge Devices 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection. object detection deep learning computer vision border patrol Electronics Haifeng Ling verfasserin aut Jianhu Yuan verfasserin aut Linyuan Bai verfasserin aut In Electronics MDPI AG, 2013 11(2022), 22, p 3828 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:22, p 3828 https://doi.org/10.3390/electronics11223828 kostenfrei https://doaj.org/article/9f60e7c9a48e4c57aabc5c3656f21297 kostenfrei https://www.mdpi.com/2079-9292/11/22/3828 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 3828
allfieldsSound	10.3390/electronics11223828 doi (DE-627)DOAJ085591270 (DE-599)DOAJ9f60e7c9a48e4c57aabc5c3656f21297 DE-627 ger DE-627 rakwb eng TK7800-8360 Lei Yue verfasserin aut A Lightweight Border Patrol Object Detection Network for Edge Devices 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection. object detection deep learning computer vision border patrol Electronics Haifeng Ling verfasserin aut Jianhu Yuan verfasserin aut Linyuan Bai verfasserin aut In Electronics MDPI AG, 2013 11(2022), 22, p 3828 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:22, p 3828 https://doi.org/10.3390/electronics11223828 kostenfrei https://doaj.org/article/9f60e7c9a48e4c57aabc5c3656f21297 kostenfrei https://www.mdpi.com/2079-9292/11/22/3828 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 3828
language	English
source	In Electronics 11(2022), 22, p 3828 volume:11 year:2022 number:22, p 3828
sourceStr	In Electronics 11(2022), 22, p 3828 volume:11 year:2022 number:22, p 3828
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	object detection deep learning computer vision border patrol Electronics
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container_title	Electronics
authorswithroles_txt_mv	Lei Yue @@aut@@ Haifeng Ling @@aut@@ Jianhu Yuan @@aut@@ Linyuan Bai @@aut@@
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callnumber-first	T - Technology
author	Lei Yue
spellingShingle	Lei Yue misc TK7800-8360 misc object detection misc deep learning misc computer vision misc border patrol misc Electronics A Lightweight Border Patrol Object Detection Network for Edge Devices
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topic_title	TK7800-8360 A Lightweight Border Patrol Object Detection Network for Edge Devices object detection deep learning computer vision border patrol
topic	misc TK7800-8360 misc object detection misc deep learning misc computer vision misc border patrol misc Electronics
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title	A Lightweight Border Patrol Object Detection Network for Edge Devices
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title_full	A Lightweight Border Patrol Object Detection Network for Edge Devices
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title_sort	lightweight border patrol object detection network for edge devices
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title_auth	A Lightweight Border Patrol Object Detection Network for Edge Devices
abstract	Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection.
abstractGer	Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection.
abstract_unstemmed	Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection.
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title_short	A Lightweight Border Patrol Object Detection Network for Edge Devices
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