张 平,佟昆宏,王学珍.基于改进 U-net 网络的液压管路分割方法[J].电子测量与仪器学报,2023,37(1):123-129
基于改进 U-net 网络的液压管路分割方法
Hydraulic pipeline segmentation method based on improved U-net network
  
DOI:10.13382/j.issn.1000-7105.2023.01.014
中文关键词:  液压管路  图像分割  Mobilenetv3 网络  注意力机制  SC 自校正模块
英文关键词:hydraulic line  image segmentation  Mobilenetv3 network  mechanism of attention  SC self-correcting module
基金项目:陕西省液压技术重点实验室开放基金(YYJS2022KF08)、陕西省工业科技攻关项目 (2015GY068)资助
作者单位
张 平 1. 西安建筑科技大学机电工程学院 
佟昆宏 1. 西安建筑科技大学机电工程学院 
王学珍 2. 上海宝冶集团有限公司 
AuthorInstitution
Zhang Ping 1. College of Mechanical & Electrical Engineering,Xi′an University of Architecture and Technology 
Tong Kunhong 1. College of Mechanical & Electrical Engineering,Xi′an University of Architecture and Technology 
Wang Xuezhen 2. Shanghai Baoye Group Co. , Ltd. 
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中文摘要:
      针对液压管路背景多变、管道弯折、管道重叠排布等复杂现象且现有图像分割方法对管路分割精度不高等问题,提出一 种以 U-net 网络为基础,结合 Mobilenetv3 网络、 SE 注意力机制模块、自校正卷积模块的液压管路分割方法。 该方法以 Mobilenetv3-large 模型作为骨干网络,结合 LR-ASPP 网络处理特征图;在解码过程中,融入 SE 注意力模块和 SC 自校正模块,提 升了特征提取能力;最后采用 Dice 函数和 BCE 函数的组合来作为网络的损失函数,有效地提升了网络的收敛能力。 实验结果 表明本文提出的方法在交并比、像素精度指标上的均值分别达到 90. 8%、95. 2%,且模型体积为 16. 9 M,推理每张图像所耗时间 20 ms,可应用于需实时部署的场景,为液压管路渗漏的准确识别提供了基础。
英文摘要:
      Aiming at the complex phenomena such as variable background, bending and overlapping arrangement of hydraulic pipeline and the low accuracy of pipeline segmentation by existing image segmentation methods, a hydraulic pipeline segmentation method based on U-net network, combined with Mobilenetv3 network, squeeze-and-excitation networks module and self-calibration convolutional module is proposed. The method selected Mobilenetv3-large model as the backbone network, and the feature maps are processed with Lraspp network. In the decoding process, the squeeze-and-excitation networks module and self-correction module are integrated to improve the feature extraction ability. Finally, the combination of Dice function and BCE function is used as the loss function of the network, which effectively improves the convergence ability of the network. Experimental results show that the mean values of the proposed method in the intersection over union and pixel accuracy are 90. 8% and 95. 2%, respectively. The model size is 16. 9 M, and the reasoning time for each image is 20 ms, which can be applied to the scene requiring real-time deployment. It provides a basis for the accurate identification of hydraulic pipeline leakage.
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