MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms
The Loess Plateau, with its fragile ecological environment and frequent landslides, poses severe risks to both ecological safety and human life. Accurate and efficient landslide detection is essential for disaster prevention and sustainable regional development. This study proposes an enhanced targe...
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| Format: | Article |
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Taylor & Francis Group
2025-12-01
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| Collection: | International Journal of Digital Earth |
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| Accès en ligne: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2536666 |
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| author | Yuan Liang Zhe Chen Zhengbo Yu Zhong Wang Qingyun Ji Ziqiong He Dongsheng Zhong Zhongchang Sun Huadong Guo |
| author_facet | Yuan Liang Zhe Chen Zhengbo Yu Zhong Wang Qingyun Ji Ziqiong He Dongsheng Zhong Zhongchang Sun Huadong Guo |
| author_sort | Yuan Liang |
| collection | DOAJ |
| description | The Loess Plateau, with its fragile ecological environment and frequent landslides, poses severe risks to both ecological safety and human life. Accurate and efficient landslide detection is essential for disaster prevention and sustainable regional development. This study proposes an enhanced target detection architecture, MAH-YOLO, designed for the precise identification of eolian landslides in complex environments. The MAH-YOLO model integrates multi-attention mechanisms (Halo Attention and Global Attention) with a lightweight design (MobileNetv3) to timely optimise landslide detection with high precision. Performance evaluations against Faster-RCNN and YOLOv8n demonstrated substantial improvements in key metrics, including precision (95.68%), recall (80.13%), and mean average precision (mAP) at 88.25%. These metrics were 5.43, 6.01 percentage points, 10.88, 4.96 percentage points, and 5.04, 4.87 percentage points higher than Faster-RCNN and YOLOv8n, respectively. Additionally, MAH-YOLO, a low complexity model with only 3.35M parameters and 16.1G FLOPs was used, balancing efficiency and accuracy. The ablation experiments confirmed the effectiveness of both the multi-attention mechanism and lightweight design. The proposed MAH-YOLO architecture excels at capturing intricate textures and detailed features, offering reliable support for accurate landslide detection. Our work provides valuable insights for intelligent monitoring and early warning systems of geological disasters on the Loess Plateau. |
| format | Article |
| id | doaj-art-bcc4a3aee4994e21a565cc153ea4615e |
| institution | Matheson Library |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-bcc4a3aee4994e21a565cc153ea4615e2025-07-23T07:16:21ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2025.2536666MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanismsYuan Liang0Zhe Chen1Zhengbo Yu2Zhong Wang3Qingyun Ji4Ziqiong He5Dongsheng Zhong6Zhongchang Sun7Huadong Guo8School of Mathematical Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaSchool of Mathematical Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaSchool of Earth Science and Resources, China University of Geosciences (Beijing), Beijing, People’s Republic of ChinaCollege of Management Science, Chengdu University of Technology, Chengdu, People’s Republic of ChinaSchool of Mathematical Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Management Science, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaInternational Research Centre of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInternational Research Centre of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaThe Loess Plateau, with its fragile ecological environment and frequent landslides, poses severe risks to both ecological safety and human life. Accurate and efficient landslide detection is essential for disaster prevention and sustainable regional development. This study proposes an enhanced target detection architecture, MAH-YOLO, designed for the precise identification of eolian landslides in complex environments. The MAH-YOLO model integrates multi-attention mechanisms (Halo Attention and Global Attention) with a lightweight design (MobileNetv3) to timely optimise landslide detection with high precision. Performance evaluations against Faster-RCNN and YOLOv8n demonstrated substantial improvements in key metrics, including precision (95.68%), recall (80.13%), and mean average precision (mAP) at 88.25%. These metrics were 5.43, 6.01 percentage points, 10.88, 4.96 percentage points, and 5.04, 4.87 percentage points higher than Faster-RCNN and YOLOv8n, respectively. Additionally, MAH-YOLO, a low complexity model with only 3.35M parameters and 16.1G FLOPs was used, balancing efficiency and accuracy. The ablation experiments confirmed the effectiveness of both the multi-attention mechanism and lightweight design. The proposed MAH-YOLO architecture excels at capturing intricate textures and detailed features, offering reliable support for accurate landslide detection. Our work provides valuable insights for intelligent monitoring and early warning systems of geological disasters on the Loess Plateau.https://www.tandfonline.com/doi/10.1080/17538947.2025.2536666MAH-YOLOloess landslide detectionmulti-attention mechanismYOLOv8lightweight models |
| spellingShingle | Yuan Liang Zhe Chen Zhengbo Yu Zhong Wang Qingyun Ji Ziqiong He Dongsheng Zhong Zhongchang Sun Huadong Guo MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms International Journal of Digital Earth MAH-YOLO loess landslide detection multi-attention mechanism YOLOv8 lightweight models |
| title | MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms |
| title_full | MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms |
| title_fullStr | MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms |
| title_full_unstemmed | MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms |
| title_short | MAH-YOLO: an enhanced YOLOv8n framework for loess landslide detection with multi-attention mechanisms |
| title_sort | mah yolo an enhanced yolov8n framework for loess landslide detection with multi attention mechanisms |
| topic | MAH-YOLO loess landslide detection multi-attention mechanism YOLOv8 lightweight models |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2536666 |
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