Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching
Aiming at the small imaging, complex background and crowded distribution of remote sensing image targets, a remote sensing image target detection algorithm (HQ-S2ANet) based on perceptual extension and anchor frame optimal matching is proposed by using the rotating target detection method S2ANet as...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2023-08-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2023/04/jnwpu2023414p820/jnwpu2023414p820.html |
| _version_ | 1797343910684524544 |
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| author | HONG Wei ZHAO Xiangmo WANG Peng LI Xiaoyan DI Ruohai LYU Zhigang WANG Chu |
| author_facet | HONG Wei ZHAO Xiangmo WANG Peng LI Xiaoyan DI Ruohai LYU Zhigang WANG Chu |
| author_sort | HONG Wei |
| collection | DOAJ |
| description | Aiming at the small imaging, complex background and crowded distribution of remote sensing image targets, a remote sensing image target detection algorithm (HQ-S2ANet) based on perceptual extension and anchor frame optimal matching is proposed by using the rotating target detection method S2ANet as a baseline network. Firstly, a cooperative attention(SEA) module is built to capture the relationship among the feature pixels when extending the model perception area to realize the relationship modeling between the target and the global. Secondly, the feature pyramid (FPN) feature fusion process is improved to form a perceptual extension feature pyramid module (HQFPN), which guarantees the low-level detail position information in the down sampling process when extending the perception area to enhance the model information capturing capability. Finally, a high-quality anchor frame is used to detect the target by using the high quality anchor frame as the baseline network. The high-quality anchor frame matching method (MaxIoUAssigner_HQ) is used to control the anchor frame truth value assignment by using a constant factor to ensure the recall rate while preventing the generation of low-quality anchor frame matching. The experimental results show that, under the DOTA dataset, the average accuracy(mAP) of HQ-S2ANet is improved by 3.1%, the parameters number increased by only 2.61M and the average recall(recall) is improved by 1.6% compared with the S2ANet algorithm, and the present algorithm effectively enhances the detection capability of the remote sensing image target. |
| first_indexed | 2024-03-08T10:54:39Z |
| format | Article |
| id | doaj.art-c9d77faaa6704f7598b28fdbbf8e5ba5 |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-08T10:54:39Z |
| publishDate | 2023-08-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-c9d77faaa6704f7598b28fdbbf8e5ba52024-01-26T16:39:24ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252023-08-0141482083010.1051/jnwpu/20234140820jnwpu2023414p820Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matchingHONG Wei0ZHAO Xiangmo1WANG Peng2LI Xiaoyan3DI Ruohai4LYU Zhigang5WANG Chu6School of Ordnance Science and Technological, Xi'an Technological UniversitySchool of Electronic Information Engineering, Xi'an Technological UniversityDevelopment Planning Service, Xi'an Technological UniversitySchool of Electronic Information Engineering, Xi'an Technological UniversitySchool of Electronic Information Engineering, Xi'an Technological UniversitySchool of Electronic Information Engineering, Xi'an Technological UniversitySchool of Electronic Information Engineering, Xi'an Technological UniversityAiming at the small imaging, complex background and crowded distribution of remote sensing image targets, a remote sensing image target detection algorithm (HQ-S2ANet) based on perceptual extension and anchor frame optimal matching is proposed by using the rotating target detection method S2ANet as a baseline network. Firstly, a cooperative attention(SEA) module is built to capture the relationship among the feature pixels when extending the model perception area to realize the relationship modeling between the target and the global. Secondly, the feature pyramid (FPN) feature fusion process is improved to form a perceptual extension feature pyramid module (HQFPN), which guarantees the low-level detail position information in the down sampling process when extending the perception area to enhance the model information capturing capability. Finally, a high-quality anchor frame is used to detect the target by using the high quality anchor frame as the baseline network. The high-quality anchor frame matching method (MaxIoUAssigner_HQ) is used to control the anchor frame truth value assignment by using a constant factor to ensure the recall rate while preventing the generation of low-quality anchor frame matching. The experimental results show that, under the DOTA dataset, the average accuracy(mAP) of HQ-S2ANet is improved by 3.1%, the parameters number increased by only 2.61M and the average recall(recall) is improved by 1.6% compared with the S2ANet algorithm, and the present algorithm effectively enhances the detection capability of the remote sensing image target.https://www.jnwpu.org/articles/jnwpu/full_html/2023/04/jnwpu2023414p820/jnwpu2023414p820.html遥感图像特征融合锚框匹配旋转检测 |
| spellingShingle | HONG Wei ZHAO Xiangmo WANG Peng LI Xiaoyan DI Ruohai LYU Zhigang WANG Chu Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching Xibei Gongye Daxue Xuebao 遥感图像 特征融合 锚框匹配 旋转检测 |
| title | Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching |
| title_full | Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching |
| title_fullStr | Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching |
| title_full_unstemmed | Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching |
| title_short | Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching |
| title_sort | remote sensing target detection algorithm based on perceptual extension and anchor frame best fit matching |
| topic | 遥感图像 特征融合 锚框匹配 旋转检测 |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2023/04/jnwpu2023414p820/jnwpu2023414p820.html |
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