Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers
Ensuring the operational safety of power transmission corridors is critical for maintaining a stable electricity supply. However, their geospatial complexity makes them increasingly vulnerable to wildfires. Wildfires vary dramatically in scale—from pixel-level ignition to kilometer-wide s...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11079617/ |
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| author | Yicen Liu Xi Liu Zhikun Zheng Shimin Luo Yongjun Xiao Shuxian Wang Xiaojiang Liu Zhenhong Sun |
| author_facet | Yicen Liu Xi Liu Zhikun Zheng Shimin Luo Yongjun Xiao Shuxian Wang Xiaojiang Liu Zhenhong Sun |
| author_sort | Yicen Liu |
| collection | DOAJ |
| description | Ensuring the operational safety of power transmission corridors is critical for maintaining a stable electricity supply. However, their geospatial complexity makes them increasingly vulnerable to wildfires. Wildfires vary dramatically in scale—from pixel-level ignition to kilometer-wide spread—and often occur in visually cluttered environments. These conditions challenge existing Transformer- and YOLO-based detectors, which struggle with multi-scale feature conflicts and fine-grained inter-class distinctions. To address this, we propose a unified wildfire detection framework incorporating two key modules: the Multi-scale Adaptive Texture Encoding (MATE) module and the Dynamic Feature Pyramid Integration (DFPI) module. MATE leverages heterogeneous receptive fields to capture wildfire patterns across spatial scales, while DFPI enhances feature discrimination via scale-aware attention and adaptive resolution fusion. Additionally, we construct a large-scale wildfire dataset with 5,000 annotated images covering diverse terrains, weather, lighting, and fire stages. Extensive experiments demonstrate that our method achieves superior performance, with an mAP50 of 72.9% and an mAP50-95 of 39.3%, significantly outperforming state-of-the-art baselines in detecting wildfires under real-world, cluttered conditions. |
| format | Article |
| id | doaj-art-a421c1276f4848e0b081b4d23d5992f3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a421c1276f4848e0b081b4d23d5992f32025-08-20T03:07:10ZengIEEEIEEE Access2169-35362025-01-011314097314098310.1109/ACCESS.2025.358851711079617Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on TransformersYicen Liu0Xi Liu1Zhikun Zheng2https://orcid.org/0009-0008-6469-8280Shimin Luo3Yongjun Xiao4Shuxian Wang5Xiaojiang Liu6Zhenhong Sun7State Grid Sichuan Electric Power Research Institute, Chengdu, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaEnsuring the operational safety of power transmission corridors is critical for maintaining a stable electricity supply. However, their geospatial complexity makes them increasingly vulnerable to wildfires. Wildfires vary dramatically in scale—from pixel-level ignition to kilometer-wide spread—and often occur in visually cluttered environments. These conditions challenge existing Transformer- and YOLO-based detectors, which struggle with multi-scale feature conflicts and fine-grained inter-class distinctions. To address this, we propose a unified wildfire detection framework incorporating two key modules: the Multi-scale Adaptive Texture Encoding (MATE) module and the Dynamic Feature Pyramid Integration (DFPI) module. MATE leverages heterogeneous receptive fields to capture wildfire patterns across spatial scales, while DFPI enhances feature discrimination via scale-aware attention and adaptive resolution fusion. Additionally, we construct a large-scale wildfire dataset with 5,000 annotated images covering diverse terrains, weather, lighting, and fire stages. Extensive experiments demonstrate that our method achieves superior performance, with an mAP50 of 72.9% and an mAP50-95 of 39.3%, significantly outperforming state-of-the-art baselines in detecting wildfires under real-world, cluttered conditions.https://ieeexplore.ieee.org/document/11079617/Power transmission corridors wildfire detectionmulti-scale fusionadaptive texturetransformer |
| spellingShingle | Yicen Liu Xi Liu Zhikun Zheng Shimin Luo Yongjun Xiao Shuxian Wang Xiaojiang Liu Zhenhong Sun Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers IEEE Access Power transmission corridors wildfire detection multi-scale fusion adaptive texture transformer |
| title | Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers |
| title_full | Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers |
| title_fullStr | Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers |
| title_full_unstemmed | Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers |
| title_short | Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers |
| title_sort | power transmission corridors wildfire detection for multi scale fusion and adaptive texture learning based on transformers |
| topic | Power transmission corridors wildfire detection multi-scale fusion adaptive texture transformer |
| url | https://ieeexplore.ieee.org/document/11079617/ |
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