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基于改进DeepLabV3+的煤尘图像分割方法

左纯子 王征 张科 潘红光

左纯子,王征,张科,等. 基于改进DeepLabV3+的煤尘图像分割方法[J]. 工矿自动化,2022,48(5):52-57, 64.  doi: 10.13272/j.issn.1671-251x.2021120086
引用本文: 左纯子,王征,张科,等. 基于改进DeepLabV3+的煤尘图像分割方法[J]. 工矿自动化,2022,48(5):52-57, 64.  doi: 10.13272/j.issn.1671-251x.2021120086
ZUO Chunzi, WANG Zheng, ZHANG Ke, et al. Coal dust image segmentation method based on improved DeepLabV3+[J]. Journal of Mine Automation,2022,48(5):52-57, 64.  doi: 10.13272/j.issn.1671-251x.2021120086
Citation: ZUO Chunzi, WANG Zheng, ZHANG Ke, et al. Coal dust image segmentation method based on improved DeepLabV3+[J]. Journal of Mine Automation,2022,48(5):52-57, 64.  doi: 10.13272/j.issn.1671-251x.2021120086

基于改进DeepLabV3+的煤尘图像分割方法

doi: 10.13272/j.issn.1671-251x.2021120086
基金项目: 国家自然科学基金资助项目(51804249)。
详细信息
    作者简介:

    左纯子(1996—),女,陕西西安人,硕士研究生,研究方向为图像处理,E-mail:1220630049@qq.com

  • 中图分类号: TD67

Coal dust image segmentation method based on improved DeepLabV3+

  • 摘要: 采用传统的语义分割网络对煤尘颗粒这种较小的目标进行分割时存在深层信息易丢失、细节提取不明显等问题。针对该问题,提出了一种基于改进DeepLabV3+的煤尘图像分割方法。从3个方面对DeepLabV3+网络模型进行改进:① 在编码器中,用CA−MobileNetV3轻量化模块代替Xception实现特征提取,确保特征提取更加细致、准确。② 在空洞空间卷积池化金字塔(ASPP)模块中对空洞率进行改进,使其更适合小颗粒煤尘提取。③ 在解码器中引入全局注意力上采样(GAU)模块,在计算量较小时对低层特征信息进行加权,用高层特征信息指导低层特征信息,实现特征融合。GAU模块用全局上采样机制代替解码器的上采样机制,使煤尘颗粒的特征信息经过长距离传输后不衰减,更加有利于捕捉煤尘颗粒的边缘细节信息。实验结果表明,改进DeepLabV3+网络模型在煤尘数据集上的召回率为90.26%,准确度为89.23%,相比于其他网络模型,改进DeepLabV3+对煤尘特征的学习能力更强,能获取更多细节信息,并大幅缩短训练时间,对小目标的分割效果更优。

     

  • 图  1  改进DeepLabV3+网络模型结构

    Figure  1.  The structure of improved DeepLabV3+ network model

    图  2  CA−MobileNetV3模块结构

    Figure  2.  The structure of CA-MobileNetV3 module

    图  3  GAU模块结构

    Figure  3.  The structure of GAU module

    图  4  图像特征提取结果

    Figure  4.  Image characteristic extraction results

    图  5  不同网络模型对煤尘图像的分割效果

    Figure  5.  Segmentation effect of different network models on coal dust image

    表  1  不同特征提取网络的性能

    Table  1.   Performance of different characteristic extraction networks

    特征提取
    网络
    MIoU/%运行
    时间/h
    模型大
    小/MB
    CA−MobileNetV372.361.28507.18
    Xception78.431.01320.87
    下载: 导出CSV

    表  2  不同空洞率下DeepLabV3+网络模型的分割性能

    Table  2.   Segmentation performance of DeepLabV3+ network model under different dilation rates

    空洞率PA/%MIoU/%
    [1,6,8,12]84.2356.53
    [1,12,18,24]84.5356.23
    [1,5,7,11]86.6360.03
    [1,7,11,13]85.2658.73
    [1,3,7,9]85.3658.63
    下载: 导出CSV

    表  3  GAU模块性能

    Table  3.   The performance of GAU module

    网络模型MIoU/%准确度/%
    未引入GAU模块72.3684.13
    引入GAU模块76.5685.27
    下载: 导出CSV

    表  4  各网络模型性能指标

    Table  4.   Performance indicators of each network model

    网络模型召回率%准确度%F1/%MIoU/%耗时/h占用内
    存/GB
    U−Net85.3482.3283.80811.508.7
    Unet−SE87.2183.2985.23841.328.6
    SegNet86.2379.0382.47831.478.9
    PSPNet85.7883.1684.45891.358.1
    FCN84.4578.9681.61821.417.7
    DeepLabV3+86.6784.1385.38901.258.0
    改进
    DeepLabV3+
    90.2689.2389.74931.027.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-27
  • 修回日期:  2022-04-25
  • 网络出版日期:  2022-03-05

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