Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation
The precise detection of military targets under complex conditions is a key factor to enhance the ability of war situation generation and prediction. The current technology can not overcome the problems of smoke and occlusion interference, target height change, and different scales in aerial video....
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2023-12-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2023/06/jnwpu2023416p1179/jnwpu2023416p1179.html |
| _version_ | 1797289620456603648 |
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| author | GAO Wuqi YANG Ting LI Liangliang |
| author_facet | GAO Wuqi YANG Ting LI Liangliang |
| author_sort | GAO Wuqi |
| collection | DOAJ |
| description | The precise detection of military targets under complex conditions is a key factor to enhance the ability of war situation generation and prediction. The current technology can not overcome the problems of smoke and occlusion interference, target height change, and different scales in aerial video. In this paper, a multi feature cross fusion and cross layer cascade aerial military target detection algorithm (YOLOv5-MFLC) is proposed. Firstly, aiming at the high confidentiality of the military targets and the shortage of battlefield aerial image resources, a real scene based aerial military target dataset is constructed, and the methods of random splicing and random extraction embedding are used for data enhancement in order to improve the diversity and generalization of targets. Secondly, aiming at the problem of complex background interference, a multi feature cross fusion attention mechanism is constructed to enhance the available information of target features. Finally, for the multi-scale problem of targets in aerial images, a cross layer cascaded multi-scale feature fusion pyramid is designed to improve the detection accuracy of cross scale targets. The experimental results show that, comparing with the existing advanced detection models, the detection accuracy of the algorithm in this paper has been greatly improved. The average accuracy of the algorithm can reach 81.0%, which is 5.2% higher than the original network. In particular, it has reached 55.9% in the smaller target category "person", which is 9.4% higher. And the experimental results further show the usefulness of the improved algorithm for small target detection. At the same time, the detection rate of this algorithm can reach 56 frame/s, which can effectively achieve accurate and fast detection of battlefield targets, and has certain experience value for guiding complex modern wars. |
| first_indexed | 2024-03-07T19:07:45Z |
| format | Article |
| id | doaj.art-bb9a181488f847719410d4d56b608270 |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-07T19:07:45Z |
| publishDate | 2023-12-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-bb9a181488f847719410d4d56b6082702024-03-01T07:59:43ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252023-12-014161179118910.1051/jnwpu/20234161179jnwpu2023416p1179Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenationGAO Wuqi0YANG Ting1LI Liangliang2School of Computer Science and Engineering, Xi'an University of TechnologySchool of Weapon Science and Technology, Xi'an University of TechnologySchool of Mechanical and Electrical Engineering, Xi'an University of TechnologyThe precise detection of military targets under complex conditions is a key factor to enhance the ability of war situation generation and prediction. The current technology can not overcome the problems of smoke and occlusion interference, target height change, and different scales in aerial video. In this paper, a multi feature cross fusion and cross layer cascade aerial military target detection algorithm (YOLOv5-MFLC) is proposed. Firstly, aiming at the high confidentiality of the military targets and the shortage of battlefield aerial image resources, a real scene based aerial military target dataset is constructed, and the methods of random splicing and random extraction embedding are used for data enhancement in order to improve the diversity and generalization of targets. Secondly, aiming at the problem of complex background interference, a multi feature cross fusion attention mechanism is constructed to enhance the available information of target features. Finally, for the multi-scale problem of targets in aerial images, a cross layer cascaded multi-scale feature fusion pyramid is designed to improve the detection accuracy of cross scale targets. The experimental results show that, comparing with the existing advanced detection models, the detection accuracy of the algorithm in this paper has been greatly improved. The average accuracy of the algorithm can reach 81.0%, which is 5.2% higher than the original network. In particular, it has reached 55.9% in the smaller target category "person", which is 9.4% higher. And the experimental results further show the usefulness of the improved algorithm for small target detection. At the same time, the detection rate of this algorithm can reach 56 frame/s, which can effectively achieve accurate and fast detection of battlefield targets, and has certain experience value for guiding complex modern wars.https://www.jnwpu.org/articles/jnwpu/full_html/2023/06/jnwpu2023416p1179/jnwpu2023416p1179.htmlaerial imagetarget detectionyolov5fusion attention mechanismmultiscale characteristic pyramid |
| spellingShingle | GAO Wuqi YANG Ting LI Liangliang Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation Xibei Gongye Daxue Xuebao aerial image target detection yolov5 fusion attention mechanism multiscale characteristic pyramid |
| title | Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation |
| title_full | Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation |
| title_fullStr | Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation |
| title_full_unstemmed | Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation |
| title_short | Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation |
| title_sort | aerial military target detection algorithm based on multi feature cross fusion and cross layer concatenation |
| topic | aerial image target detection yolov5 fusion attention mechanism multiscale characteristic pyramid |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2023/06/jnwpu2023416p1179/jnwpu2023416p1179.html |
| work_keys_str_mv | AT gaowuqi aerialmilitarytargetdetectionalgorithmbasedonmultifeaturecrossfusionandcrosslayerconcatenation AT yangting aerialmilitarytargetdetectionalgorithmbasedonmultifeaturecrossfusionandcrosslayerconcatenation AT liliangliang aerialmilitarytargetdetectionalgorithmbasedonmultifeaturecrossfusionandcrosslayerconcatenation |