RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes
Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the ap...
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/6/1001 |
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| author | Keda Li Xiangyue Zheng Jingxin Bi Gang Zhang Yi Cui Tao Lei |
| author_facet | Keda Li Xiangyue Zheng Jingxin Bi Gang Zhang Yi Cui Tao Lei |
| author_sort | Keda Li |
| collection | DOAJ |
| description | Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. To overcome this limitation, we propose RMVAD-YOLO, a multi-view aircraft detection model built upon YOLOv8. First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features of the same aircraft category from multiple views while enhancing feature differentiation between different aircraft categories. Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. Additionally, to further leverage background information and enhance the model’s adaptability to multi-scale target variations, we incorporate the LSK Module into the backbone network. Finally, we propose the WFMIoUv3 loss function, which strengthens the model’s focus on challenging samples and improves detection robustness. Experimental results on the newly released Multi-Perspective Aircraft Dataset (MAD) demonstrate that RMVAD-YOLO achieves an accuracy of 90.1%, a recall of 76%, 84.8% mAP@0.5, and 70.5% mAP@0.5:0.95, while reducing parameters and delivering an overall improvement in detection performance compared to the baseline YOLOv8n. RMVAD-YOLO also performed well on the VisDrone 2019 dataset, further demonstrating its reliable generalization capabilities. |
| format | Article |
| id | doaj-art-03881fbb865f47ccb5965f67912d8db0 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-03881fbb865f47ccb5965f67912d8db02025-08-20T01:48:54ZengMDPI AGRemote Sensing2072-42922025-03-01176100110.3390/rs17061001RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar ClassesKeda Li0Xiangyue Zheng1Jingxin Bi2Gang Zhang3Yi Cui4Tao Lei5National Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaAircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. To overcome this limitation, we propose RMVAD-YOLO, a multi-view aircraft detection model built upon YOLOv8. First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features of the same aircraft category from multiple views while enhancing feature differentiation between different aircraft categories. Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. Additionally, to further leverage background information and enhance the model’s adaptability to multi-scale target variations, we incorporate the LSK Module into the backbone network. Finally, we propose the WFMIoUv3 loss function, which strengthens the model’s focus on challenging samples and improves detection robustness. Experimental results on the newly released Multi-Perspective Aircraft Dataset (MAD) demonstrate that RMVAD-YOLO achieves an accuracy of 90.1%, a recall of 76%, 84.8% mAP@0.5, and 70.5% mAP@0.5:0.95, while reducing parameters and delivering an overall improvement in detection performance compared to the baseline YOLOv8n. RMVAD-YOLO also performed well on the VisDrone 2019 dataset, further demonstrating its reliable generalization capabilities.https://www.mdpi.com/2072-4292/17/6/1001multi-view aircraft detectionaircraft classificationfeature fusionsmall target |
| spellingShingle | Keda Li Xiangyue Zheng Jingxin Bi Gang Zhang Yi Cui Tao Lei RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes Remote Sensing multi-view aircraft detection aircraft classification feature fusion small target |
| title | RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes |
| title_full | RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes |
| title_fullStr | RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes |
| title_full_unstemmed | RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes |
| title_short | RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes |
| title_sort | rmvad yolo a robust multi view aircraft detection model for imbalanced and similar classes |
| topic | multi-view aircraft detection aircraft classification feature fusion small target |
| url | https://www.mdpi.com/2072-4292/17/6/1001 |
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