A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target...
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MDPI AG
2025-08-01
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| author | Qi Wang Guanghu Xu Donglin Jing |
| author_facet | Qi Wang Guanghu Xu Donglin Jing |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. |
| format | Article |
| id | doaj-art-0c699c8da58f4a39917eb693aa7677ca |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-0c699c8da58f4a39917eb693aa7677ca2025-08-20T03:04:42ZengMDPI AGApplied Sciences2076-34172025-08-011515872710.3390/app15158727A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical ExpansionQi Wang0Guanghu Xu1Donglin Jing2School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRemote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection.https://www.mdpi.com/2076-3417/15/15/8727depthwise separable convolutionprogressive scale variationtargets with diverse orientations |
| spellingShingle | Qi Wang Guanghu Xu Donglin Jing A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion Applied Sciences depthwise separable convolution progressive scale variation targets with diverse orientations |
| title | A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion |
| title_full | A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion |
| title_fullStr | A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion |
| title_full_unstemmed | A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion |
| title_short | A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion |
| title_sort | rotation target detection network based on multi kernel interaction and hierarchical expansion |
| topic | depthwise separable convolution progressive scale variation targets with diverse orientations |
| url | https://www.mdpi.com/2076-3417/15/15/8727 |
| work_keys_str_mv | AT qiwang arotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion AT guanghuxu arotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion AT donglinjing arotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion AT qiwang rotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion AT guanghuxu rotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion AT donglinjing rotationtargetdetectionnetworkbasedonmultikernelinteractionandhierarchicalexpansion |