Cumulus Clouds Under Varying Lighting Conditions.
<div><p>Automatic recognition of ground-based clouds is crucial for meteorology and especially for the operational safety of Unmanned Aerial Vehicles (UAVs), but it is challenged by variable cloud shapes, complex lighting, and background interference. This paper introduces ALGA-DenseNet,...
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2025
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| _version_ | 1852015334591037440 |
|---|---|
| author | Binbin Tu (1499677) |
| author2 | Haoyuan Zhou (22530459) Xiaowei Han (5219507) Jiawei Bao (17737830) Linfei Zhao (4099297) Nanmu Hui (22530462) |
| author2_role | author author author author author |
| author_facet | Binbin Tu (1499677) Haoyuan Zhou (22530459) Xiaowei Han (5219507) Jiawei Bao (17737830) Linfei Zhao (4099297) Nanmu Hui (22530462) |
| author_role | author |
| dc.creator.none.fl_str_mv | Binbin Tu (1499677) Haoyuan Zhou (22530459) Xiaowei Han (5219507) Jiawei Bao (17737830) Linfei Zhao (4099297) Nanmu Hui (22530462) |
| dc.date.none.fl_str_mv | 2025-10-30T17:41:23Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0333999.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Cumulus_Clouds_Under_Varying_Lighting_Conditions_/30492903 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Evolutionary Biology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles tianjin normal university model &# 8217 incorporates adaptive local enhancing feature selection enhance image robustness depthwise separable convolutions scale feature extraction scale attention mechanism paper introduces alga improved densenet model variable cloud shapes complex cloud features based cloud dataset scale extraction cloud textures complex lighting based clouds merge features head attention global attention color features vision transformer uavs ), reducing parameters operational safety integrates mixed improve learning enhances representation computational complexity class variations class differences background interference |
| dc.title.none.fl_str_mv | Cumulus Clouds Under Varying Lighting Conditions. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Automatic recognition of ground-based clouds is crucial for meteorology and especially for the operational safety of Unmanned Aerial Vehicles (UAVs), but it is challenged by variable cloud shapes, complex lighting, and background interference. This paper introduces ALGA-DenseNet, an improved DenseNet model with a multi-scale attention mechanism. The model employs Color Jitter to enhance image robustness and improve learning of intra-class variations and inter-class differences. It incorporates Adaptive Local and Global Attention (ALGA) to merge features, enhancing feature selection. Additionally, it integrates mixed and depthwise separable convolutions to optimize multi-scale feature extraction, reducing parameters and computational complexity. Furthermore, integrating a Vision Transformer (ViT) and Dynamic Multi-head Attention (DMA) enhances representation of complex cloud features. Experimental results show recognition accuracies of 97.94% on the TJNU (Tianjin Normal University) Ground-based Cloud Dataset (GCD) and 97.25% on the Cirrus Cumulus Stratus Nimbus (CCSN) dataset. This indicates the model’s capability for fine-grained, multi-scale extraction of cloud textures, shapes, and color features, along with strong generalization performance.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e9dac2e99d4414a7bb9155b5bc47fc76 |
| identifier_str_mv | 10.1371/journal.pone.0333999.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30492903 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cumulus Clouds Under Varying Lighting Conditions.Binbin Tu (1499677)Haoyuan Zhou (22530459)Xiaowei Han (5219507)Jiawei Bao (17737830)Linfei Zhao (4099297)Nanmu Hui (22530462)Evolutionary BiologyPlant BiologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedunmanned aerial vehiclestianjin normal universitymodel &# 8217incorporates adaptive localenhancing feature selectionenhance image robustnessdepthwise separable convolutionsscale feature extractionscale attention mechanismpaper introduces algaimproved densenet modelvariable cloud shapescomplex cloud featuresbased cloud datasetscale extractioncloud texturescomplex lightingbased cloudsmerge featureshead attentionglobal attentioncolor featuresvision transformeruavs ),reducing parametersoperational safetyintegrates mixedimprove learningenhances representationcomputational complexityclass variationsclass differencesbackground interference<div><p>Automatic recognition of ground-based clouds is crucial for meteorology and especially for the operational safety of Unmanned Aerial Vehicles (UAVs), but it is challenged by variable cloud shapes, complex lighting, and background interference. This paper introduces ALGA-DenseNet, an improved DenseNet model with a multi-scale attention mechanism. The model employs Color Jitter to enhance image robustness and improve learning of intra-class variations and inter-class differences. It incorporates Adaptive Local and Global Attention (ALGA) to merge features, enhancing feature selection. Additionally, it integrates mixed and depthwise separable convolutions to optimize multi-scale feature extraction, reducing parameters and computational complexity. Furthermore, integrating a Vision Transformer (ViT) and Dynamic Multi-head Attention (DMA) enhances representation of complex cloud features. Experimental results show recognition accuracies of 97.94% on the TJNU (Tianjin Normal University) Ground-based Cloud Dataset (GCD) and 97.25% on the Cirrus Cumulus Stratus Nimbus (CCSN) dataset. This indicates the model’s capability for fine-grained, multi-scale extraction of cloud textures, shapes, and color features, along with strong generalization performance.</p></div>2025-10-30T17:41:23ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0333999.g002https://figshare.com/articles/figure/Cumulus_Clouds_Under_Varying_Lighting_Conditions_/30492903CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304929032025-10-30T17:41:23Z |
| spellingShingle | Cumulus Clouds Under Varying Lighting Conditions. Binbin Tu (1499677) Evolutionary Biology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles tianjin normal university model &# 8217 incorporates adaptive local enhancing feature selection enhance image robustness depthwise separable convolutions scale feature extraction scale attention mechanism paper introduces alga improved densenet model variable cloud shapes complex cloud features based cloud dataset scale extraction cloud textures complex lighting based clouds merge features head attention global attention color features vision transformer uavs ), reducing parameters operational safety integrates mixed improve learning enhances representation computational complexity class variations class differences background interference |
| status_str | publishedVersion |
| title | Cumulus Clouds Under Varying Lighting Conditions. |
| title_full | Cumulus Clouds Under Varying Lighting Conditions. |
| title_fullStr | Cumulus Clouds Under Varying Lighting Conditions. |
| title_full_unstemmed | Cumulus Clouds Under Varying Lighting Conditions. |
| title_short | Cumulus Clouds Under Varying Lighting Conditions. |
| title_sort | Cumulus Clouds Under Varying Lighting Conditions. |
| topic | Evolutionary Biology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles tianjin normal university model &# 8217 incorporates adaptive local enhancing feature selection enhance image robustness depthwise separable convolutions scale feature extraction scale attention mechanism paper introduces alga improved densenet model variable cloud shapes complex cloud features based cloud dataset scale extraction cloud textures complex lighting based clouds merge features head attention global attention color features vision transformer uavs ), reducing parameters operational safety integrates mixed improve learning enhances representation computational complexity class variations class differences background interference |