彭道刚,刘薇薇,戚尔江,胡 捷.基于 CBAM-Res_UNet 电厂高压蒸汽泄漏检测研究[J].电子测量与仪器学报,2021,35(12):206-214
基于 CBAM-Res_UNet 电厂高压蒸汽泄漏检测研究
Research on leakage detection of high pressure steam inpower plant based on CBAM-Res_Unet
  
DOI:
中文关键词:  电厂高压蒸汽泄漏检测  CBAM-Res_UNet 图像分割网络  损失函数 Focal Loss+Dice Loss  性能指标 F1_score
英文关键词:detection of high pressure steam leakage in power plant  CBAM-Res_UNet image segmentation network  Loss function Focal Loss+Dice Loss  performance index F1_score
基金项目:上海市“科技创新行动计划”高新技术领域项目(21511101800)资助
作者单位
彭道刚 1. 上海电力大学自动化工程学院 
刘薇薇 1. 上海电力大学自动化工程学院 
戚尔江 1. 上海电力大学自动化工程学院 
胡 捷 2. 宝山钢铁股份有限公司能源环保部电厂 
AuthorInstitution
Peng Daogang 1. Faculty of Automation Engineering, Shanghai University of Electric Power 
Liu Weiwei 1. Faculty of Automation Engineering, Shanghai University of Electric Power 
Qi Erjiang 1. Faculty of Automation Engineering, Shanghai University of Electric Power 
Hu Jie 2. Power plant of Baoshan Iron & Steel Co. , Ltd 
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中文摘要:
      发电厂高压蒸汽泄漏检测关乎电厂设备长期稳定运行。 为了提高电厂高压蒸汽泄漏检测的准确性,解决泄漏区域的错 分割和漏分割问题,提出基于 CBAM-Res_UNet 图像分割网络的电厂高压蒸汽泄漏检测算法,在 UNet 结构中加入 ResNet 的残 差块 residual_block 来获取泄漏图像更多的语义信息,并且融入 CBAM,加强高压蒸汽泄漏图像区域特征的学习,网络再根据不 同损失函数和评价标准对图像分割结果的影响,选择损失函数 Focal Loss+Dice Loss 和性能指标 F1_score。 通过在电厂高压蒸 汽泄漏图像数据集上进行实验,CBAM-Res_UNet 网络得到的 F1_score 值为 0. 985,实验结果表明,该网络可以更加完整的分割 出蒸汽泄漏区域,对高压蒸汽泄漏图像多样性有较强的泛化能力。
英文摘要:
      The detection of high pressure steam leakage in power plant is related to the long-term stable operation of power plant equipment. In order to improve the accuracy of high-pressure steam leakage detection in power plants and solve the problem of wrong segmentation and leakage segmentation of leakage areas, this paper proposes a high-pressure steam leakage detection algorithm based on CBAM-Res_UNet image segmentation network. The residual_block of ResNet is added to the UNet structure to obtain more semantic information of leakage images, and CBAM is integrated to strengthen the learning of regional characteristics of high-pressure steam leakage images. According to the influence of different loss functions and evaluation criteria on image segmentation results, the loss function Focal Loss+Dice Loss and performance index F1_score are selected. Through the experiment on the image data set of highpressure steam leakage in power plant, the F1_score obtained by CBAM-Res_UNet network is 0. 985. The experimental results show that the network can more completely segment the steam leakage area, and has a strong generalization ability for the variety of high pressure steam leakage images.
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