|本期目录/Table of Contents|

[1]凌晓,王昕越,郭凯,等.基于UNet模型燃气管道高后果区分割方法研究*[J].中国安全生产科学技术,2024,20(4):157-162.[doi:10.11731/j.issn.1673-193x.2024.04.022]
　LING Xiao,WANG Xinyue,GUO Kai,et al.Research on segmentation method for high-consequence areas of gas pipeline based on UNet model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(4):157-162.[doi:10.11731/j.issn.1673-193x.2024.04.022]
点击复制

基于UNet模型燃气管道高后果区分割方法研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年4期
页码:
157-162
栏目:
职业安全卫生管理与技术
出版日期:
2024-04-30

文章信息/Info

Title:
Research on segmentation method for high-consequence areas of gas pipeline based on UNet model
文章编号:
1673-193X(2024)-04-0157-06
作者:
凌晓; 王昕越; 郭凯; 孙宝财; 程凌宇
（1.兰州理工大学 石油化工学院，甘肃 兰州 730050;
2.甘肃省特种设备检验检测研究院，甘肃 兰州 730050）
Author(s):
LING Xiao;  WANG Xinyue;  GUO Kai;  SUN Baocai;  CHENG Lingyu
(1.College of Petrochemical Technology,Lanzhou University of Technology,Lanzhou Gansu 730050,China;
2.Gansu Special Equipment Inspection and Testing Institute,Lanzhou Gansu 730050,China)
关键词:
深度学习; UNet模型; 卷积神经网络; 高后果区; 图像分割
Keywords:
deep learning;  UNet model;  convolutional neural network;  high-consequence area;  image segmentation
分类号:
X937
DOI:
10.11731/j.issn.1673-193x.2024.04.022
文献标志码:
A
摘要:
为提升燃气管道设施监测和事故应急响应中的高后果区图像分割精准度和可靠性，通过改进UNet模型结构，使用优化后的Inception Block模块、通道注意力和空间注意力机制的方法，提升模型捕捉关键特征的能力，并引入高斯噪声增强模型鲁棒性，采用保留最佳参数策略得到最优训练参数。然后对SE UNet、UNet++、原始UNet以及改进后UNet模型在航拍图像数据集上的分割效果进行对比和分析。研究结果表明：相对SE UNet、UNet++和原始UNet，改进后UNet模型在分割效果上表现更佳，综合性能优于其他模型。同时，改进后UNet模型提高了分割准确性，降低了误检和漏检风险。研究结果可为燃气管道设施的安全运行和维护提供有力支持。
Abstract:
In order to improve the accuracy and reliability of high-consequence area image segmentation in gas pipeline facility monitoring and emergency response,the UNet model was improved and optimized After the InceptionBlock module,channel attention and spatial attention mechanism methods,the model’s ability to capture key features is improved,and Gaussian noise is introduced to enhance the model robustness The optimal training parameters are obtained by using the strategy of preserving the best parameters.Then,the segmentation effects of SE UNet,UNet++,original UNet and improved UNet models on aerial image data sets are compared and analyzed.The results show that compared with SE UNet,UNet++ and the original UNet,the improved UNet model is efficient in segmentation The results show better performance,and the comprehensive performance is better than other models.At the same time,the improved UNet model improves the segmentation accuracy and reduces the risk of false detection and missing detection.The results can be flammable Provide strong support for the safe operation and maintenance of gas pipeline facilities.

参考文献/References:

［1］曹亚利,李振雷,刘旭东,等.基于卷积神经网络的冲击地压预警方法研究［J］.中国安全生产科学技术,2022,18(10):101-108. CAO Yali,LI Zhenlei,LIU Xudong,et al.Research on early-warning method of rockburst based on convolutional neural network ［J］.Journal of Safety Science and Technology,2022,18(10):101-108.
［2］李子奇,蒋柱虎,王力,等.基于深度学习的工程结构损伤识别研究进展［J］.中国安全生产科学技术,2022,18(12):43-48. LI Ziqi,JIANG Zhuhu,WANG Li,et al.Research progress in damage identification of engineering structure based on deep learning ［J］.Journal of Safety Science and Technology,2022,18(12):43-48.
［3］SHEN J,CHEN H,XU M X,et al.Intelligent image segmentation model for remote sensing applications［J］.Journal of Intelligent and Fuzzy Systems,2019,37(1):361-370.
［4］CHEN X,LI D F,LIU M Z,et al.CNN and transformer fusion for remote sensing image semantic segmentation［J］.Remote Sensing,2023,15(18):4455.
［5］靳晓,王立娟,王东升,等.基于高分遥感的危险化学品重大危险源安全布局动态管控研究［J］.中国安全生产科学技术,2020,16(8):44-50. JIN Xiao,WANG Lijuan,WANG Dongsheng,et al.Research on dynamic management and control of safety layout for major hazard installations of hazardous chemicals based on high resolution remote sensing ［J］.Journal of Safety Science and Technology,2020,16(8):44-50.
［6］LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.
［7］RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation［C］//Germany:Springer International Publishing,2015.
［8］苏健民,杨岚心,景维鹏.基于U-Net的高分辨率遥感图像语义分割方法［J］.计算机工程与应用,2019(7):207-213. SU Jianmin,YANG Lanxin,JING Weipeng.U-Net based semantic segmentation method for high resolution remote sensing image ［J］.Computer Engineering and Applications,2019（7）:207-213.
［9］赵晋陵,詹媛媛,王娟,等.基于SE-UNet的冬小麦种植区域提取方法［J］.农业机械学报,2022,53(9):189-196. ZHAO Jinling,ZHAN Yuanyuan,WANG Juan,et al.SE-UNet-Based extraction of winter wheat planting areas ［J］.Transactions of the Chinese Society for Agricultural Machinery,2022,53(9):189-196.
［10］吕秉略,李忠峰,奚峥皓,等.基于UNet++的煤岩显微图像组分分析［J］.计算机与数字工程,2022，50(2):389-393. LYU Binglue,LI Zhongfeng,XI Zhenghao,et al.Composition analysis of coal and rock microscopic images based on UNet++［J］.Computer and Digital Engineering,2022,50(2):389-393.
［11］季顺平,魏世清.遥感影像建筑物提取的卷积神经元网络与开源数据集方法［J］.测绘学报,2019,48(4):448-459. JI Shunping,WEI Shiqing.Convolutional neural networks and open source dataset methods for extracting buildings from remote sensing images［J］.Journal of Surveying and Mapping,2019,48(4):448-459.
［12］SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015:1-9.
［13］杨振舰,尚佳美,张众维，等.基于残差注意力机制的图像去雾算法［J］.西北工业大学学报,2021,39(4):901-908. YANG Zhenjian,SHANG Jiamei,ZHANG Zhongwei,et al.A new end-to-end image dehazing algorithm based on residual attention mechanism［J］.Journal of Northwestern Polytechnical University,2021,39(4):901-908.
［14］谢谦,董立红,吴雪菲.基于多源数据融合的煤矿工作面瓦斯浓度预测［J］.中国安全生产科学技术,2022,18(11):71-76. XIE Qian,DONG Lihong,WU Xuefei.Prediction of gas concentration in coal mining face based on multi-source data fusion ［J］.Journal of Safety Science and Technology,2022,18(11):71-76.
［15］XIE Z Y,SHU C,FU Y,et al.Balanced loss function for accurate surface defect segmentation［J］.Applied Sciences,2023,13(2):826-826.

相似文献/References:

[1]涂思羽,彭平安,蒋元建.基于深度学习的井下环境异常工况智能识别技术研究[J].中国安全生产科学技术,2018,14(11):58.[doi:10.11731/j.issn.1673-193x.2018.11.009]
　TU Siyu,PENG Pingan,JIANG Yuanjian.Research on intelligent recognition technology of abnormal operating conditions in underground environment based on deep learning method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(4):58.[doi:10.11731/j.issn.1673-193x.2018.11.009]
[2]刘欣宜,张宝峰,符烨,等.基于深度学习的污染场地作业人员着装规范性检测[J].中国安全生产科学技术,2020,16(7):169.[doi:10.11731/j.issn.1673-193x.2020.07.027]
　LIU Xinyi,ZHANG Baofeng,FU Ye,et al.Detection on normalization of operating personnel dressing at contaminated sites based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(4):169.[doi:10.11731/j.issn.1673-193x.2020.07.027]
[3]毕东月.基于深度学习的输煤皮带故障视觉检测方法研究[J].中国安全生产科学技术,2021,17(8):84.[doi:10.11731/j.issn.1673-193x.2021.08.013]
　BI Dongyue.Research on visual detection method for fault of coal conveyor belt based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(4):84.[doi:10.11731/j.issn.1673-193x.2021.08.013]
[4]崔铁军,王凌霄.YOLOv4目标检测算法在煤矿工人口罩佩戴监测工作中的应用研究*[J].中国安全生产科学技术,2021,17(10):66.[doi:10.11731/j.issn.1673-193x.2021.10.010]
　CUI Tiejun,WANG Lingxiao.Research on application of YOLOv4 object detection algorithm in monitoring on masks wearing of coal miners[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(4):66.[doi:10.11731/j.issn.1673-193x.2021.10.010]
[5]刘超,雷晨,李树刚,等.基于CNN-GRU的瓦斯浓度预测模型及应用*[J].中国安全生产科学技术,2022,18(9):62.[doi:10.11731/j.issn.1673-193x.2022.09.009]
　LIU Chao,LEI Chen,LI Shugang,et al.Prediction model of gas concentration based on CNN-GRU and its application[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(4):62.[doi:10.11731/j.issn.1673-193x.2022.09.009]
[6]曹亚利,李振雷,刘旭东,等.基于卷积神经网络的冲击地压预警方法研究*[J].中国安全生产科学技术,2022,18(10):101.[doi:10.11731/j.issn.1673-193x.2022.10.015]
　CAO Yali,LI Zhenlei,LIU Xudong,et al.Research on early-warning method of rockburst based on convolutional neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(4):101.[doi:10.11731/j.issn.1673-193x.2022.10.015]
[7]李子奇,蒋柱虎,王力,等.基于深度学习的工程结构损伤识别研究进展[J].中国安全生产科学技术,2022,18(12):43.[doi:10.11731/j.issn.1673-193x.2022.12.006]
　LI Ziqi,JIANG Zhuhu,WANG Li,et al.Research progress in damage identification of engineering structure based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(4):43.[doi:10.11731/j.issn.1673-193x.2022.12.006]
[8]唐豪,奉鑫鑫,高曙,等.基于视频分析的空管员违规行为识别方法*[J].中国安全生产科学技术,2023,19(1):196.[doi:10.11731/j.issn.1673-193x.2023.01.029]
　TANG Hao,FENG Xinxin,GAO Shu,et al.Violations recognition method of air traffic controllers based on video analysis[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(4):196.[doi:10.11731/j.issn.1673-193x.2023.01.029]
[9]黄鹏,房志明,朱曼,等.基于SSD网络的电梯内电动自行车检测研究*[J].中国安全生产科学技术,2023,19(2):167.[doi:10.11731/j.issn.1673-193x.2023.02.023]
　HUANG Peng,FANG Zhiming,ZHU Man,et al.Research on detection of electric bicycles in elevator based on SSD network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(4):167.[doi:10.11731/j.issn.1673-193x.2023.02.023]
[10]夏正洪,何琥,吴建军,等.基于深度学习的航空铆钉分类及异常情况检测*[J].中国安全生产科学技术,2023,19(6):199.[doi:10.11731/j.issn.1673-193x.2023.06.028]
　XIA Zhenghong,HE Hu,WU Jianjun,et al.Aviation rivet classification and abnormal situation detection based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(4):199.[doi:10.11731/j.issn.1673-193x.2023.06.028]

备注/Memo

备注/Memo:
收稿日期： 2023-11-23
* 基金项目： 国家自然科学基金项目（52204074）；甘肃省科技计划项目（23YFGA0059）
作者简介： 凌晓，博士，副教授，主要研究方向为油气储运设施安全保障技术。
更新日期/Last Update: 2024-05-09