多线激光光条图像缺陷分割模型研究

郭晓轩; 冯其波; 冀振燕; 郑发家; 杨燕燕

doi:10.12263/DZXB.20220644

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多线激光光条图像缺陷分割模型研究

学术论文 | 更新时间：2025-07-02

- 多线激光光条图像缺陷分割模型研究
- Research on Segmentation Model of Multi-Line Laser Strip Image’s Defects
- 电子学报 2023年51卷第1期页码：172-179
- 作者机构：
  
  1.北京交通大学物理科学与工程学院，北京 100044
  2.北京交通大学软件学院，北京 100044
  3.东莞市诺丽科技股份有限公司，广东东莞 523050
- 作者简介：
  
  [ "郭晓轩男，1996年4月出生，山东人.北京交通大学物理科学与工程学院博士研究生.主要研究方向为机器视觉和计算机视觉.E-mail: 20118037@bjtu.edu.cn" ]
  [ "冯其波（通讯作者）男，1962年5月出生，广东人.博士.北京交通大学物理科学与工程学院教授，博士生导师.研究方向为光学测量、铁路安全测量技术、仪器仪表、机器视觉等." ]
  [ "冀振燕女，1972年4月出生，河南人.博士.北京交通大学软件学院副教授，博士生导师.主要研究方向为计算机视觉、多源异构数据融合等.E-mail: zhyji@bjtu.edu.cn" ]
  [ "郑发家男，1991年2月出生，安徽人.2021年于北京交通大学获得博士学位，现为北京交通大学讲师.主要研究方向为数控机床几何误差测量、轮对踏面几何参数在线测量、机器视觉.E-mail: zhfajia@bjtu.edu.cn" ]
  [ "杨燕燕女，1986年3月出生，河南人.北京交通大学讲师.主要研究方向为机器学习、不确定性人工智能.E-mail: yangyy@bjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金重点项目(51935002);国家自然科学基金面上项目(52175493)
- DOI：10.12263/DZXB.20220644
  中图分类号： TP183;
- 收稿：2022-06-02，
  
  修回：2022-08-10，
  
  纸质出版：2023-01-25
- 稿件说明：
移动端阅览
郭晓轩,冯其波,冀振燕等.多线激光光条图像缺陷分割模型研究[J].电子学报,2023,51(01):172-179.

GUO Xiao-xuan,FENG Qi-bo,JI Zhen-yan,et al.Research on Segmentation Model of Multi-Line Laser Strip Image’s Defects[J].ACTA ELECTRONICA SINICA,2023,51(01):172-179.
郭晓轩,冯其波,冀振燕等.多线激光光条图像缺陷分割模型研究[J].电子学报,2023,51(01):172-179. DOI： 10.12263/DZXB.20220644.

GUO Xiao-xuan,FENG Qi-bo,JI Zhen-yan,et al.Research on Segmentation Model of Multi-Line Laser Strip Image’s Defects[J].ACTA ELECTRONICA SINICA,2023,51(01):172-179. DOI： 10.12263/DZXB.20220644.

摘要

受环境干扰以及反射光影响，室外采集的多线激光光条图像含有光斑和断裂缺陷.为了准确地分割图像缺陷，本文提出了一个轻量的UT（U-shape Target，U代表U型编解码网络结构，T代表靶形视野）分割模型，模型由3×3卷积和靶形卷积堆叠而成.靶形卷积是针对激光光条图像特点提出的多视野卷积模块，模块中四个卷积分支构成靶形卷积视野，能够提取激光光条图像几何结构特征、局部细节特征以及环绕纹理特征.实验表明，UT模型在多线激光光条图像上的缺陷分割精度高于主流分割模型，而且实现了分割精度和参数量的平衡.

Abstract

Influenced by environmental interference and reflected light

multi-line laser strip images collected outdoors contain the defects of flares and fractures. In order to segment the defects accurately

this paper proposes light-weight UT (U-shape Target

U represents a U-shaped encoder-decoder network architecture

and T represents a target-shaped receptive field) segmentation model

which stacks 3 × 3 convolutions and target convolutions. Considering the characteristics of laser strip images

we propose the target convolution

a multiple-receptive-field convolution module. Four convolution branches in this module form a target-shaped convolution receptive field

which can extract the geometric structure features

the local detail features and the surrounding texture features from the laser strip images. Experiments show that the UT model has higher defect segmentation accuracy than mainstream segmentation models

and can achieve the balance between the segmentation accuracy and the number of parameters.

关键词

Keywords

references

徐频捷 , 陈逸杰 , 李之南 , 等 . 基于事件驱动的车道线识别算法研究 [J]. 电子学报 , 2021 , 49 ( 7 ): 1379 - 1385 .

XU P J , CHEN Y J , LI Z N , et al . Research on event-driven lane recognition algorithms [J]. Acta Electronica Sinica , 2021 , 49 ( 7 ): 1379 - 1385 . (in Chinese)

赖小波 , 许茂盛 , 徐小媚 . 多分类CNN的胶质母细胞瘤多模态MR图像分割 [J]. 电子学报 , 2019 , 47 ( 8 ): 1738 - 1747 .

LAI X B , XU M S , XU X M . Glioblastoma multiforme multi-modal MR images segmentation using multi-class CNN [J]. Acta Electronica Sinica , 2019 , 47 ( 8 ): 1738 - 1747 . (in Chinese)

付利华 , 赵宇 , 姜涵煦 , 等 . 基于前景感知视觉注意的半监督视频目标分割 [J]. 电子学报 , 2022 , 50 ( 1 ): 195 - 206 .

FU L H , ZHAO Y , JIANG H X , et al . Semi-supervised video object segmentation based on foreground perception visual attention [J]. Acta Electronica Sinica , 2022 , 50 ( 1 ): 195 - 206 . (in Chinese)

YUAN Y H , CHEN X L , WANG J D . Object-contextual representations for semantic segmentation [C]// European Conference on Computer Vision . Glasgow : Springer , 2020 : 173 - 190 .

SUN K , XIAO B , LIU D , et al . Deep high-resolution representation learning for human pose estimation [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 5686 - 5696 .

SZEGEDY C , LIU W , JIA Y Q , et al . Going deeper with convolutions [C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston : IEEE , 2015 : 1 - 9 .

CHEN L C , PAPANDREOU G , KOKKINOS I , et al . DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 40 ( 4 ): 834 - 848 .

WANG Y , ZHOU Q , LIU J , et al . Lednet: A lightweight encoder-decoder network for real-time semantic segmentation [C]// 2019 IEEE International Conference on Image Processing . Taipei : IEEE , 2019 : 1860 - 1864 .

HONG Y D , PAN H H , SUN W C , et al . Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes [EB/OL]. ( 2021-01-15 )[ 2022-06 ]. https://arxiv.org/abs/2101.06085 https://arxiv.org/abs/2101.06085 .

PENG J C , LIU Y , TANG S Y , et al . PP-LiteSeg: A superior real-time semantic segmentation model [EB/OL]. ( 2022-04-06 )[ 2022-06 ]. https://arxiv.org/abs/2204.02681 https://arxiv.org/abs/2204.02681 .

GAO R . Rethink dilated convolution for real-time semantic segmentation [EB/OL]. ( 2021-11-18 )[ 2022-06 ]. https://arxiv.org/abs/2111.09957 https://arxiv.org/abs/2111.09957 .

YU C Q , GAO C X , WANG J B , et al . BiSeNet V2: Bilateral network with guided aggregation for real-time semantic segmentation [J]. International Journal of Computer Vision , 2021 , 129 ( 11 ): 3051 - 3068 .

ZHAO H S , QI X J , SHEN X Y , et al . ICNet for real-time semantic segmentation on high-resolution images [C]// European Conference on Computer Vision . Munich : Springer , 2018 : 418 - 434 .

RONNEBERGER O , FISCHER P , BROX T . U-Net: Convolutional networks for biomedical image segmentation [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich : Springer , 2015 : 234 - 241 .

HUANG H M , LIN L F , TONG R F , et al . UNet 3: A full-scale connected UNet for medical image segmentation [C]// ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing . Barcelona : IEEE , 2020 : 1055 - 1059 .

LIU S T , HUANG D , WANG Y H . Receptive field block net for accurate and fast object detection [C]// European Conference on Computer Vision . Munich : Springer , 2018 : 404 - 419 .

WANG P Q , CHEN P F , YUAN Y , et al . Understanding convolution for semantic segmentation [C]// 2018 IEEE Winter Conference on Applications of Computer Vision . Lake Tahoe : IEEE , 2018 : 1451 - 1460 .

WU T Y , TANG S , ZHANG R , et al . CGNet: A light-weight context guided network for semantic segmentation [J]. IEEE Transactions on Image Processing , 2021 , 30 : 1169 - 1179 .

DING X H , GUO Y C , DING G G , et al . ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks [C]// 2019 IEEE/CVF International Conference on Computer Vision . Seoul : IEEE , 2019 : 1911 - 1920 .

ZHOU Z W , RAHMAN SIDDIQUEE M M , TAJBAKHSH N , et al . UNet++: A nested U-Net architecture for medical image segmentation [C]// Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Granada : Springer , 2018 : 3 - 11 .

OKTAY O , SCHLEMPER J , FOLGOC L L , et al . Attention U-net: Learning where to look for the pancreas [EB/OL].( 2018-04-11 )[ 2022-06 ]. https://arxiv.org/abs/1804.03999 https://arxiv.org/abs/1804.03999 .

LI H C , XIONG P F , FAN H Q , et al . DFANet: deep feature aggregation for real-time semantic segmentation [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 9514 - 9523 .

PASZKE A , CHAURASIA A , KIM S , et al . ENet: A deep neural network architecture for real-time semantic segmentation [EB/OL]. ( 2016-06-07 )[ 2022-06 ]. https://arxiv.org/abs/1606.02147 https://arxiv.org/abs/1606.02147 .

MEHTA S , RASTEGARI M , SHAPIRO L , et al . ESPNetv2: A light-weight, power efficient, and general purpose convolutional neural network [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 9182 - 9192 .

YU C Q , WANG J B , PENG C , et al . BiSeNet: Bilateral segmentation network for real-time semantic segmentation [C]// European Conference on Computer Vision . Munich : Springer , 2018 : 334 - 349 .

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