MF-YOLO: Mask Wearing Detection Algorithm for Dense Environments Article Swipe
Peng Wen
,
Zhengyi Yuan
,
Junhu Zhang
,
Haitao Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3551892
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3551892
To address the challenges of false positives and missed detections in face mask detection within dense environments, we propose the MF-YOLO face mask detection model. First, a feature map convolution approach is introduced to transform the feature map information into the initial weights of convolutional kernels, accelerating the convergence of model training. Additionally, a residual self-attention module is designed to capture fine edge position features by combining global feature extraction with sliding window-based information interaction, effectively reducing missed detections, especially for small objects. Furthermore, we introduce a mixed attention mechanism that integrates both channel and spatial attention, enabling the model to focus more effectively on key features and better target the objects of interest. Finally, an enhanced feature-fusion strategy is employed to mitigate the loss of feature information during layer-by-layer transmission, improving the model’s ability to fuse information at the same scale. Experimental results in the public MASK dataset and the self-built SFMD dataset demonstrate that the proposed algorithm significantly accelerates convergence while improving accuracy by 2.1% and 1.3%, respectively, compared to the original network. Our code and datasets are publicly released on GitHub https://github.com/wenpengshishuaige/MF-YOLO.
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- https://doi.org/10.1109/access.2025.3551892
- OA Status
- gold
- References
- 46
- Related Works
- 10
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https://openalex.org/W4408520140Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3551892Digital Object Identifier
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MF-YOLO: Mask Wearing Detection Algorithm for Dense EnvironmentsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Peng Wen, Zhengyi Yuan, Junhu Zhang, Haitao LiList of authors in order
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https://doi.org/10.1109/access.2025.3551892Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3551892Direct OA link when available
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Computer science, Algorithm, Computer visionTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.designed | 58 |
| abstract_inverted_index.employed | 120 |
| abstract_inverted_index.enabling | 97 |
| abstract_inverted_index.enhanced | 116 |
| abstract_inverted_index.features | 64, 106 |
| abstract_inverted_index.kernels, | 45 |
| abstract_inverted_index.mitigate | 122 |
| abstract_inverted_index.network. | 174 |
| abstract_inverted_index.objects. | 82 |
| abstract_inverted_index.original | 173 |
| abstract_inverted_index.position | 63 |
| abstract_inverted_index.proposed | 157 |
| abstract_inverted_index.publicly | 180 |
| abstract_inverted_index.reducing | 76 |
| abstract_inverted_index.released | 181 |
| abstract_inverted_index.residual | 54 |
| abstract_inverted_index.strategy | 118 |
| abstract_inverted_index.algorithm | 158 |
| abstract_inverted_index.attention | 88 |
| abstract_inverted_index.combining | 66 |
| abstract_inverted_index.detection | 13, 23 |
| abstract_inverted_index.improving | 131, 163 |
| abstract_inverted_index.interest. | 113 |
| abstract_inverted_index.introduce | 85 |
| abstract_inverted_index.mechanism | 89 |
| abstract_inverted_index.positives | 6 |
| abstract_inverted_index.training. | 51 |
| abstract_inverted_index.transform | 34 |
| abstract_inverted_index.attention, | 96 |
| abstract_inverted_index.challenges | 3 |
| abstract_inverted_index.detections | 9 |
| abstract_inverted_index.especially | 79 |
| abstract_inverted_index.extraction | 69 |
| abstract_inverted_index.integrates | 91 |
| abstract_inverted_index.introduced | 32 |
| abstract_inverted_index.self-built | 151 |
| abstract_inverted_index.accelerates | 160 |
| abstract_inverted_index.convergence | 48, 161 |
| abstract_inverted_index.convolution | 29 |
| abstract_inverted_index.demonstrate | 154 |
| abstract_inverted_index.detections, | 78 |
| abstract_inverted_index.effectively | 75, 103 |
| abstract_inverted_index.information | 38, 73, 127, 137 |
| abstract_inverted_index.Experimental | 142 |
| abstract_inverted_index.Furthermore, | 83 |
| abstract_inverted_index.accelerating | 46 |
| abstract_inverted_index.interaction, | 74 |
| abstract_inverted_index.window-based | 72 |
| abstract_inverted_index.Additionally, | 52 |
| abstract_inverted_index.convolutional | 44 |
| abstract_inverted_index.environments, | 16 |
| abstract_inverted_index.respectively, | 169 |
| abstract_inverted_index.significantly | 159 |
| abstract_inverted_index.transmission, | 130 |
| abstract_inverted_index.feature-fusion | 117 |
| abstract_inverted_index.layer-by-layer | 129 |
| abstract_inverted_index.model’s | 133 |
| abstract_inverted_index.self-attention | 55 |
| abstract_inverted_index.<uri>https://github.com/wenpengshishuaige/MF-YOLO</uri>. | 184 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.07364101 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |