A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios
Abstract Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Sc...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86601-y |
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| author | Siyu Li Fei Yan Yunqing Liu Yuzhuo Shen Lan Liu Ke Wang |
| author_facet | Siyu Li Fei Yan Yunqing Liu Yuzhuo Shen Lan Liu Ke Wang |
| author_sort | Siyu Li |
| collection | DOAJ |
| description | Abstract Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Scale Rotated Detection Network (MSRO-Net) for detecting rotated ship targets in remote sensing images. The network adopts a CNN-Transformer hybrid architecture for collaborative feature extraction and integrates our proposed Coordinate-Aware Pyramid Feature Aggregation module (CAPP). This backbone network retains the capability of local feature extraction while also connecting global context and capturing long-range dependencies. The model can gather information from any position in the sequence, extract contextual information of targets at different scales, and enhance global feature representation. Additionally, this paper proposes an Upsampling Feature Reconstruction Pyramid (ARFPN-C), based on Adaptive Rotated Convolution (ARC). This network combines ARC adaptive rotating convolution with a multi-scale feature fusion mechanism to enhance the model’s perceptual capability, addressing the issues of limited target feature information and random orientation angles. The proposed algorithm is validated on two public remote sensing image datasets, HRSC2016 and DOTA. The mAP07, mAP12 on the HRSC2016 dataset, as well as the mAP in the ship category of the DOTA dataset, show a significant advantage over other commonly used object detection algorithms, with accuracies of 90.70%, 98.98%, and 89.46%, respectively. These results further validate the accuracy and effectiveness of the proposed MSRO-Net in object detection. |
| format | Article |
| id | doaj-art-7168f0a37b024786b73afe2df0d8a285 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-7168f0a37b024786b73afe2df0d8a2852025-01-26T12:33:34ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-86601-yA multi-scale rotated ship targets detection network for remote sensing images in complex scenariosSiyu Li0Fei Yan1Yunqing Liu2Yuzhuo Shen3Lan Liu4Ke Wang5School of Electronic Information and Engineering, Changchun University of Science and TechnologySchool of Electronic Information and Engineering, Changchun University of Science and TechnologySchool of Electronic Information and Engineering, Changchun University of Science and TechnologySchool of Electronic Information and Engineering, Changchun University of Science and TechnologySchool of Electronic Information and Engineering, Changchun University of Science and TechnologySchool of Electronic Information and Engineering, Changchun University of Science and TechnologyAbstract Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Scale Rotated Detection Network (MSRO-Net) for detecting rotated ship targets in remote sensing images. The network adopts a CNN-Transformer hybrid architecture for collaborative feature extraction and integrates our proposed Coordinate-Aware Pyramid Feature Aggregation module (CAPP). This backbone network retains the capability of local feature extraction while also connecting global context and capturing long-range dependencies. The model can gather information from any position in the sequence, extract contextual information of targets at different scales, and enhance global feature representation. Additionally, this paper proposes an Upsampling Feature Reconstruction Pyramid (ARFPN-C), based on Adaptive Rotated Convolution (ARC). This network combines ARC adaptive rotating convolution with a multi-scale feature fusion mechanism to enhance the model’s perceptual capability, addressing the issues of limited target feature information and random orientation angles. The proposed algorithm is validated on two public remote sensing image datasets, HRSC2016 and DOTA. The mAP07, mAP12 on the HRSC2016 dataset, as well as the mAP in the ship category of the DOTA dataset, show a significant advantage over other commonly used object detection algorithms, with accuracies of 90.70%, 98.98%, and 89.46%, respectively. These results further validate the accuracy and effectiveness of the proposed MSRO-Net in object detection.https://doi.org/10.1038/s41598-025-86601-y |
| spellingShingle | Siyu Li Fei Yan Yunqing Liu Yuzhuo Shen Lan Liu Ke Wang A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios Scientific Reports |
| title | A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios |
| title_full | A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios |
| title_fullStr | A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios |
| title_full_unstemmed | A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios |
| title_short | A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios |
| title_sort | multi scale rotated ship targets detection network for remote sensing images in complex scenarios |
| url | https://doi.org/10.1038/s41598-025-86601-y |
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