Camouflaged people detection based on a semi-supervised search identification network
Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions. With the equipment gradually moving towards intelligence, unmanned aerial vehicles (UAVs) will be widely used for integrated reconnaissance/attack...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-03-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914721001586 |
| _version_ | 1797861131364073472 |
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| author | Yang Liu Cong-qing Wang Yong-jun Zhou |
| author_facet | Yang Liu Cong-qing Wang Yong-jun Zhou |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions. With the equipment gradually moving towards intelligence, unmanned aerial vehicles (UAVs) will be widely used for integrated reconnaissance/attack in the future. The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility. However, as the camouflage has been designed to blend in with surroundings, which greatly reduces the probability of the target being discovered. Moreover, the lack of training data on camouflaged people detection will inhibit the training of a deep model. To address these problems, a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images. In this paper, the camouflaged object detection dataset (COD10K) is first supplemented according to our mission requirements, then the edge attention is utilized to enhance the boundaries based on search identification network. Further, a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy. Experiments demonstrate that the proposed semi-supervised search identification network (Semi-SINet) performs well in camouflaged people detection compared with other object detection methods. |
| first_indexed | 2024-04-09T21:57:07Z |
| format | Article |
| id | doaj.art-1f93a0ffe57e4c3abb063acbd04cc3c2 |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-04-09T21:57:07Z |
| publishDate | 2023-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-1f93a0ffe57e4c3abb063acbd04cc3c22023-03-24T04:22:12ZengKeAi Communications Co., Ltd.Defence Technology2214-91472023-03-0121176183Camouflaged people detection based on a semi-supervised search identification networkYang Liu0Cong-qing Wang1Yong-jun Zhou2College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, ChinaScience and Technology on Near-Surface Detection Laboratory, Wuxi, 214000, China; Corresponding author.Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions. With the equipment gradually moving towards intelligence, unmanned aerial vehicles (UAVs) will be widely used for integrated reconnaissance/attack in the future. The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility. However, as the camouflage has been designed to blend in with surroundings, which greatly reduces the probability of the target being discovered. Moreover, the lack of training data on camouflaged people detection will inhibit the training of a deep model. To address these problems, a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images. In this paper, the camouflaged object detection dataset (COD10K) is first supplemented according to our mission requirements, then the edge attention is utilized to enhance the boundaries based on search identification network. Further, a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy. Experiments demonstrate that the proposed semi-supervised search identification network (Semi-SINet) performs well in camouflaged people detection compared with other object detection methods.http://www.sciencedirect.com/science/article/pii/S2214914721001586Camouflaged people detectionSearch identification networkSemi-supervised learning strategyUnmanned aerial vehicles (UAVs) |
| spellingShingle | Yang Liu Cong-qing Wang Yong-jun Zhou Camouflaged people detection based on a semi-supervised search identification network Defence Technology Camouflaged people detection Search identification network Semi-supervised learning strategy Unmanned aerial vehicles (UAVs) |
| title | Camouflaged people detection based on a semi-supervised search identification network |
| title_full | Camouflaged people detection based on a semi-supervised search identification network |
| title_fullStr | Camouflaged people detection based on a semi-supervised search identification network |
| title_full_unstemmed | Camouflaged people detection based on a semi-supervised search identification network |
| title_short | Camouflaged people detection based on a semi-supervised search identification network |
| title_sort | camouflaged people detection based on a semi supervised search identification network |
| topic | Camouflaged people detection Search identification network Semi-supervised learning strategy Unmanned aerial vehicles (UAVs) |
| url | http://www.sciencedirect.com/science/article/pii/S2214914721001586 |
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