Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection
Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/21/4377 |
| _version_ | 1797511887301115904 |
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| author | Long Sun Jie Chen Dazheng Feng Mengdao Xing |
| author_facet | Long Sun Jie Chen Dazheng Feng Mengdao Xing |
| author_sort | Long Sun |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image. |
| first_indexed | 2024-03-10T05:54:17Z |
| format | Article |
| id | doaj.art-d91d4eaeafc54342a916a15a32c57ee2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:54:17Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d91d4eaeafc54342a916a15a32c57ee22023-11-22T21:32:35ZengMDPI AGRemote Sensing2072-42922021-10-011321437710.3390/rs13214377Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target DetectionLong Sun0Jie Chen1Dazheng Feng2Mengdao Xing3National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaUnmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.https://www.mdpi.com/2072-4292/13/21/4377drone videomulti-target detectionmultiple focal lengthsdeep learningtemplate matching |
| spellingShingle | Long Sun Jie Chen Dazheng Feng Mengdao Xing Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection Remote Sensing drone video multi-target detection multiple focal lengths deep learning template matching |
| title | Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection |
| title_full | Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection |
| title_fullStr | Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection |
| title_full_unstemmed | Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection |
| title_short | Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection |
| title_sort | parallel ensemble deep learning for real time remote sensing video multi target detection |
| topic | drone video multi-target detection multiple focal lengths deep learning template matching |
| url | https://www.mdpi.com/2072-4292/13/21/4377 |
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