Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles
Unmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/3/83 |
| _version_ | 1797241470648844288 |
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| author | Hang Zhang Jiangbin Zheng Chuang Song |
| author_facet | Hang Zhang Jiangbin Zheng Chuang Song |
| author_sort | Hang Zhang |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false negative (FN) target detections. Specifically, the primary challenge addressed in this study stems from the weak discriminability of features in Synthetic Aperture Radar (SAR) imaging targets, leading to a high false alarm rate in SAR target detection. Additionally, the uncontrollable false alarm rate during electro-optical proximity detection results in an elevated false alarm rate as well. Consequently, a cumulative error propagation problem arises when SAR and electro-optical observations of the same target from different perspectives occur at different times. This paper delves into the target association problem within the realm of collaborative detection involving multiple unmanned aerial vehicles. We first propose an improved triplet loss function to effectively assess the similarity of targets detected by multiple UAVs, mitigating false positives and negatives. Then, a consistent discrimination algorithm is described for targets in multi-perspective scenarios using distributed computing. We established a multi-UAV multi-target detection database to alleviate training and validation issues for algorithms in this complex scenario. Our proposed method demonstrates a superior correlation performance compared to state-of-the-art networks. |
| first_indexed | 2024-04-24T18:23:50Z |
| format | Article |
| id | doaj.art-bbb323188f96412dba2f6ad85c3e7a95 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-04-24T18:23:50Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-bbb323188f96412dba2f6ad85c3e7a952024-03-27T13:33:56ZengMDPI AGDrones2504-446X2024-02-01838310.3390/drones8030083Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial VehiclesHang Zhang0Jiangbin Zheng1Chuang Song2School of Software, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Software, Northwestern Polytechnical University, Xi’an 710072, ChinaScience and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, ChinaUnmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false negative (FN) target detections. Specifically, the primary challenge addressed in this study stems from the weak discriminability of features in Synthetic Aperture Radar (SAR) imaging targets, leading to a high false alarm rate in SAR target detection. Additionally, the uncontrollable false alarm rate during electro-optical proximity detection results in an elevated false alarm rate as well. Consequently, a cumulative error propagation problem arises when SAR and electro-optical observations of the same target from different perspectives occur at different times. This paper delves into the target association problem within the realm of collaborative detection involving multiple unmanned aerial vehicles. We first propose an improved triplet loss function to effectively assess the similarity of targets detected by multiple UAVs, mitigating false positives and negatives. Then, a consistent discrimination algorithm is described for targets in multi-perspective scenarios using distributed computing. We established a multi-UAV multi-target detection database to alleviate training and validation issues for algorithms in this complex scenario. Our proposed method demonstrates a superior correlation performance compared to state-of-the-art networks.https://www.mdpi.com/2504-446X/8/3/83unmanned aerial vehiclemulti-target recognitionmulti-objective matchingtarget trackingtarget drones |
| spellingShingle | Hang Zhang Jiangbin Zheng Chuang Song Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles Drones unmanned aerial vehicle multi-target recognition multi-objective matching target tracking target drones |
| title | Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles |
| title_full | Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles |
| title_fullStr | Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles |
| title_full_unstemmed | Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles |
| title_short | Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles |
| title_sort | multiple target matching algorithm for sar and visible light image data captured by multiple unmanned aerial vehicles |
| topic | unmanned aerial vehicle multi-target recognition multi-objective matching target tracking target drones |
| url | https://www.mdpi.com/2504-446X/8/3/83 |
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