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Classification and recognition of unsafe behaviors of tobacco warehouse personnel based on improved YOLOv11
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Wei KE1, Quanjie ZHU2, **, Changmao CHEN1, Chengyi WU1, Yan LIU1, Yanlin ZHANG3
China Safety Science Journal | 2025, 35(3) : 36 - 44
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China Safety Science Journal | 2025, 35(3): 36-44
• Safety social science and safety management •
Classification and recognition of unsafe behaviors of tobacco warehouse personnel based on improved YOLOv11
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Wei KE1, Quanjie ZHU2, **, Changmao CHEN1, Chengyi WU1, Yan LIU1, Yanlin ZHANG3
Affiliations
  • 1 Shiyan Tobacco Company，Hubei Province，Shiyan Hubei 442099，China
  • 2 School of Safety Emergency Technology and Management，North China Institute of Science and Technology，Langfang Hebei 065201，China
  • 3 Hubei Branch，China National Tobacco Corporation，Wuhan Hubei 430033，China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.1598
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To ensure the safety of personnel and property within the storage environment，the traditional YOLOv11 object detection algorithm was improved，and a method and model to identify unsafe behaviors of personnel in the complex environment of tobacco warehouses were proposed. First，a statistical analysis of common unsafe behavior types in tobacco storage was conducted，and the classification of unsafe behaviors of warehouse personnel was explored，including item-related，action-related，and area-related unsafe behaviors. Second，based on the characteristics of unsafe behaviors of warehouse personnel，a dataset augmentation and denoising preprocessing approach was proposed to enhance fine-grained feature extraction，and introduced to improve the saliency mapping of personnel behaviors. Then，the YOLOv11 algorithm was improved through functional enhancement modules and K-means++ anchor box optimization，and a fast detection method for unsafe behaviors of tobacco warehouse personnel was proposed. Finally，the proposed method's effectiveness was validated by comparing with self-built datasets and the open Microsoft COCO dataset. The results show that the method can quickly and effectively identify unsafe behaviors of warehouse personnel，with a significant improvement in recognition accuracy compared to traditional methods(accuracy rate is 94.91% and 88.69% respectively).

improved YOLOv11  /  tobacco warehouse personnel  /  unsafe behaviors  /  denoising  /  object detection
Wei KE, Quanjie ZHU, Changmao CHEN, Chengyi WU, Yan LIU, Yanlin ZHANG. Classification and recognition of unsafe behaviors of tobacco warehouse personnel based on improved YOLOv11[J]. China Safety Science Journal, 2025 , 35 (3) : 36 -44 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.1598
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Year 2025 volume 35 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.03.1598
  • Receive Date：2024-10-14
  • Online Date：2025-07-05
  • Published：2025-03-28
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History
  • Received：2024-10-14
  • Revised：2024-12-24
Funding
Affiliations
    1 Shiyan Tobacco Company，Hubei Province，Shiyan Hubei 442099，China
    2 School of Safety Emergency Technology and Management，North China Institute of Science and Technology，Langfang Hebei 065201，China
    3 Hubei Branch，China National Tobacco Corporation，Wuhan Hubei 430033，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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