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High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model
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Yuanliang JIANG1, 2, 3, Qingying REN3, 4, Yuan REN2, Haipeng LIU1, 2, 3, Shaohua DONG1, 3, **
China Safety Science Journal | 2025, 35(1) : 103 - 111
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China Safety Science Journal | 2025, 35(1): 103-111
• Safety engineering technology •
High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model
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Yuanliang JIANG1, 2, 3, Qingying REN3, 4, Yuan REN2, Haipeng LIU1, 2, 3, Shaohua DONG1, 3, **
Affiliations
  • 1 College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
  • 2 CNPC International Pipeline Company, Beijing 102206, China
  • 3 Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, China University of Petroleum (Beijing), Beijing 102249, China
  • 4 College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China
Published: 2025-01-28 doi: 10.16265/j.cnki.issn1003-3033.2025.01.0632
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An improved object detection algorithm for high-consequence areas was proposed to solve the problems of the sensitive and complex external environment of the overseas section of the China-Myanmar oil and gas pipeline, difficulty in manual inspection, and high-risk factors. Firstly, a convolutional block attention module was used to adaptively learn channel and spatial attention to enhance the network's perception and generalization capabilities. Then, focal and efficient intersection over union(Focal-EIoU) loss was used to comprehensively consider the target features and their associations to deal with the issues of class imbalance, reduce the interference of easy-to-classify samples, and enhance the robustness of the model. Finally, the improved model was used to intelligently recognize regional attributes of China-Myanmar oil and gas pipeline remote sensing images. Furthermore, the proposed YOLO model was validated against related ablation experiments. The results showed that for the feature recognition of remote sensing images of the China-Myanmar oil and gas pipeline, the proposed model reached a mean average precision (mAP) of 68.2% for the field, green space, settlement, and river. The model performance was improved by 29%, 21.6%, and 10.7% compared with YOLOv5, YOLOx, and YOLOv8, respectively.

YOLO  /  China-Myanmar oil and gas pipeline  /  remote sensing images  /  high consequence areas  /  object detection  /  intelligent identification
Yuanliang JIANG, Qingying REN, Yuan REN, Haipeng LIU, Shaohua DONG. High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model[J]. China Safety Science Journal, 2025 , 35 (1) : 103 -111 . DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0632
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Year 2025 volume 35 Issue 1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.01.0632
  • Receive Date：2024-08-16
  • Online Date：2025-07-05
  • Published：2025-01-28
Article Data
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History
  • Received：2024-08-16
  • Revised：2024-10-21
Funding
Affiliations
    1 College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
    2 CNPC International Pipeline Company, Beijing 102206, China
    3 Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, China University of Petroleum (Beijing), Beijing 102249, China
    4 College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, 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
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Genus		 种数
Number of
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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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