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Identification model of unsafe behaviors among operators in machining workshops
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Xiaofeng HU, Teng TENG, Jinming HU, Jiajun WEN
China Safety Science Journal | 2025, 35(8) : 40 - 47
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China Safety Science Journal | 2025, 35(8): 40-47
• Safety social science and safety management •
Identification model of unsafe behaviors among operators in machining workshops
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Xiaofeng HU, Teng TENG, Jinming HU, Jiajun WEN
Affiliations
  • 1School of Information and Network Security, People's Public Security University of China, Beijing 100038, China
  • 2Key Laboratory of Security Prevention and Risk Assessment, Ministry of Public Security, Beijing 100038, China
Published: 2025-08-28 doi: 10.16265/j.cnki.issn1003-3033.2025.08.0176
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To enhance the safety management of operators in machining workshops, an identification model based on YOLOv11 was constructed. The YOLOv11 model was improved by integrating the MetaFormer architecture, Mixed Aggregation Network (MANet) module, and Adaptive Feature Grid Convolution Attention (AFGC Attention) mechanism. A video dataset captured in a real workshop environment was established to validate the identification model. The results show that the improved YOLOv11 model can identify three types of behaviors, namely unattended operation, operating without a face shield, and operating without protective clothing, with F1scores exceeding 0.93 for all categories. The improved model demonstrates a significant enhancement in identifying small-sized targets, with the F1 score for identifying glove-wearing behavior increasing from 0.684 to 0.708, and the mAP@0.5 value rising from 0.604 to 0.651. The research findings may provide technical support for the identification and early warning of unsafe behaviors among operators in machining workshops.

machining workshops  /  operators  /  unsafe behaviors  /  YOLOv11  /  attention mechanism
Xiaofeng HU, Teng TENG, Jinming HU, Jiajun WEN. Identification model of unsafe behaviors among operators in machining workshops[J]. China Safety Science Journal, 2025 , 35 (8) : 40 -47 . DOI: 10.16265/j.cnki.issn1003-3033.2025.08.0176
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Year 2025 volume 35 Issue 8
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.08.0176
  • Receive Date：2025-03-15
  • Online Date：2026-07-09
  • Published：2025-08-28
Article Data
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History
  • Received：2025-03-15
  • Revised：2025-05-20
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Affiliations
    1School of Information and Network Security, People's Public Security University of China, Beijing 100038, China
    2Key Laboratory of Security Prevention and Risk Assessment, Ministry of Public Security, Beijing 100038, 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
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Percentage of total
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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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