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YOLOv5s-SimAM-ASFF-based Oil and Gas Drilling and Production Wellsite Safety Hazard Identification and Smart Wearable Device Development
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Jin-qiu HU1, 2, Lai-bin ZHANG1, 2, Yang-bai HU1, 2, *, Sheng-li CHU3, Bing-cai SUN3, Ze-sen LI1, 2
Science Technology and Engineering | 2025, 25(16) : 7004 - 7012
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Science Technology and Engineering | 2025, 25(16): 7004-7012
• Papers·Environmental and Safe Science •
YOLOv5s-SimAM-ASFF-based Oil and Gas Drilling and Production Wellsite Safety Hazard Identification and Smart Wearable Device Development
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Jin-qiu HU1, 2, Lai-bin ZHANG1, 2, Yang-bai HU1, 2, *, Sheng-li CHU3, Bing-cai SUN3, Ze-sen LI1, 2
Affiliations
  • 1 College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
  • 2 Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
  • 3 CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China
Published: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2406188
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Oil and gas drilling and production wellsites are complex and have many types of potential safety hazards, in order to improve the accuracy of the identification of potential safety hazards in wellsites, an oil and gas drilling and production wellsite potential safety hazard identification method based on improved YOLOv5 was proposed. Firstly, in order to solve the problem that the background of the picture was complex and the recognition difficulty increased, the SimAM attention mechanism was introduced in the backbone network; secondly, in order to solve the problem that the scales of the types of hidden hazards were different and there were multiple scales in one picture, the original feature fusion was replaced by adaptive spatial fusion of features (ASFF). Lastly, the hidden hazard recognition effect of the improved model was validated by comparing the model with other models. The results show that the improved YOLOv5 model improves the average accuracy value of recognition by 10.4%, and has a better recognition effect on the safety hazards of oil and gas drilling and production well sites. In order to solve the limitation of video monitoring and identification of oil and gas drilling and production wellsite safety hazards, a set of intelligent wearable device was developed, which effectively improved the portability of the identification of wellsite safety hazards.

oil and gas drilling and extraction well sites  /  safety hazards  /  image recognition  /  YOLOv5
Jin-qiu HU, Lai-bin ZHANG, Yang-bai HU, Sheng-li CHU, Bing-cai SUN, Ze-sen LI. YOLOv5s-SimAM-ASFF-based Oil and Gas Drilling and Production Wellsite Safety Hazard Identification and Smart Wearable Device Development[J]. Science Technology and Engineering, 2025 , 25 (16) : 7004 -7012 . DOI: 10.12404/j.issn.1671-1815.2406188
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Year 2025 volume 25 Issue 16
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Article Info
doi: 10.12404/j.issn.1671-1815.2406188
  • Receive Date：2024-08-19
  • Online Date：2025-07-09
  • Published：2025-06-08
Article Data
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History
  • Received：2024-08-19
  • Revised：2025-03-18
Funding
Affiliations
    1 College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
    2 Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
    3 CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China
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