AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images
Detecting farmland parcels in high-resolution remote sensing images is challenging in smallholder farming systems in China, characterized by fragmented plots, irregular shapes, and varying scales. To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland p...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2432532 |
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| author | Binyao Wang Ya’nan Zhou Weiwei Zhu Li Feng Jinke He Tianjun Wu Jiancheng Luo Xin Zhang |
| author_facet | Binyao Wang Ya’nan Zhou Weiwei Zhu Li Feng Jinke He Tianjun Wu Jiancheng Luo Xin Zhang |
| author_sort | Binyao Wang |
| collection | DOAJ |
| description | Detecting farmland parcels in high-resolution remote sensing images is challenging in smallholder farming systems in China, characterized by fragmented plots, irregular shapes, and varying scales. To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. In the feature extraction stage, the model incorporates an Adaptive Mix Attention (AMA) Block, balancing robust feature extraction with low computational overhead through spatial mixing and Efficient Multi-Scale Attention (EMA). During feature enhancement, to effectively detect targets of different scales, the Attentional Scale Sequence Fusion with P2 network (ASFP2Net) integrates the Triple Feature Encoder (TFE) module and Scale Sequence Feature Fusion (SSFF) module. In the prediction stage, a Multi-Scale Attention Head (MSAHead) enhances adaptability through multi-scale attention mechanisms. Extensive experiments on a custom-built dataset validate AAMS-YOLO's effectiveness, demonstrating notable enhancements over the baseline in mAP0.5 (2.6%) and mAP0.5:0.95 (2.2%) and surpassing other state-of-the-art algorithms. The proposed model excels in detecting small and densely overlapping objects through advanced feature fusion and multi-scale processing strategies. |
| format | Article |
| id | doaj-art-0fa2882da8bf4c65a594e2e6786488df |
| institution | OA Journals |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-0fa2882da8bf4c65a594e2e6786488df2025-08-20T01:52:42ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2432532AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing imagesBinyao Wang0Ya’nan Zhou1Weiwei Zhu2Li Feng3Jinke He4Tianjun Wu5Jiancheng Luo6Xin Zhang7Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaKey Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaKey Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaSchool of Land Engineering, Chang’an University, Xi’an, People’s Republic of ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaDetecting farmland parcels in high-resolution remote sensing images is challenging in smallholder farming systems in China, characterized by fragmented plots, irregular shapes, and varying scales. To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. In the feature extraction stage, the model incorporates an Adaptive Mix Attention (AMA) Block, balancing robust feature extraction with low computational overhead through spatial mixing and Efficient Multi-Scale Attention (EMA). During feature enhancement, to effectively detect targets of different scales, the Attentional Scale Sequence Fusion with P2 network (ASFP2Net) integrates the Triple Feature Encoder (TFE) module and Scale Sequence Feature Fusion (SSFF) module. In the prediction stage, a Multi-Scale Attention Head (MSAHead) enhances adaptability through multi-scale attention mechanisms. Extensive experiments on a custom-built dataset validate AAMS-YOLO's effectiveness, demonstrating notable enhancements over the baseline in mAP0.5 (2.6%) and mAP0.5:0.95 (2.2%) and surpassing other state-of-the-art algorithms. The proposed model excels in detecting small and densely overlapping objects through advanced feature fusion and multi-scale processing strategies.https://www.tandfonline.com/doi/10.1080/17538947.2024.2432532Farmland parcel detectionhigh-resolution remote sensing imagesYOLOattention mechanismmulti-scale object detection |
| spellingShingle | Binyao Wang Ya’nan Zhou Weiwei Zhu Li Feng Jinke He Tianjun Wu Jiancheng Luo Xin Zhang AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images International Journal of Digital Earth Farmland parcel detection high-resolution remote sensing images YOLO attention mechanism multi-scale object detection |
| title | AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images |
| title_full | AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images |
| title_fullStr | AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images |
| title_full_unstemmed | AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images |
| title_short | AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images |
| title_sort | aams yolo enhanced farmland parcel detection for high resolution remote sensing images |
| topic | Farmland parcel detection high-resolution remote sensing images YOLO attention mechanism multi-scale object detection |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2432532 |
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