Volume 48 Issue 5
May  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
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
shu

Coal dust image segmentation method based on improved DeepLabV3+

  • College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

More Information
  • Received Date: December 26, 2021
  • Revised Date: April 24, 2022
  • Available Online: March 04, 2022
    Graphical Abstract
    • Abstract
  • When the traditional semantic segmentation network is used to segment the small coal dust particles, there are some problems such as easy loss of deep information and unclear detail extraction. In order to solve this problem, a coal dust image segmentation method based on improved DeepLabV3+ is proposed. DeepLabV3+ network model is improved in three aspects. ① In the encoder, the CA-MobileNetV3 lightweight module is used to replace Xception to achieve characteristic extraction and ensure more detailed and accurate characteristic extraction. ② The atrous rate is improved in the atrous spatial pyramid pooling(ASPP) module to make it more suitable for extracting small coal dust particles. ③ A global attention up-sample(GAU) module is introduced into the decoder to weight the low-level characteristic information when the calculation amount is small. And the high-level characteristic information is used to guide the low-level characteristic information to realize characteristic fusion. The GAU module uses a global up-sampling mechanism to replace the up-sampling mechanism of the decoder. The characteristic information of the coal dust particles is not attenuated after long-distance transmission. And the method is more conducive to capture the edge detail information of the coal dust particles. The experimental results show that the recall rate of the improved DeepLabV3+ network model on the coal dust data set is 90.26%, and the accuracy is 89.23%. Compared with other network models, the improved DeepLabV3+ network model can effectively enhance the learning ability of coal dust characteristics, obtain more detailed information, greatly shorten the training time, and has better segmentation effect on small targets.
    • FullText(HTML)
    • References (17)
  • [1]
    王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采关键核心技术分析[J]. 煤炭学报,2019,44(1):34-41.

    WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41.
    [2]
    韩建国. 神华智能矿山建设关键技术研发与示范[J]. 煤炭学报,2016,41(12):3181-3189.

    HAN Jianguo. Key technology research and demonstration of intelligent mines in Shenhua Group[J]. Journal of China Coal Society,2016,41(12):3181-3189.
    [3]
    PLESSIS J J L D. Active explosion barrier performance against methane and coal dust explosions[J]. International Journal of Coal Science and Technology,2015(4):261-268.
    [4]
    张锦仁. 选煤技术的现状及发展趋势探索[J]. 内蒙古煤炭经济,2020(6):194. DOI: 10.3969/j.issn.1008-0155.2020.06.133

    ZHANG Jinren. Exploration on the status quo and development trend of coal preparation technology[J]. Inner Mongolia Coal Economy,2020(6):194. DOI: 10.3969/j.issn.1008-0155.2020.06.133
    [5]
    吴开兴,宋剑. 基于灰度共生矩阵的煤与矸石自动识别研究[J]. 煤炭工程,2016,48(2):98-101.

    WU Kaixing,SONG Jian. Automatic coal-gangue identification based on gray level co-occurrence matrix[J]. Coal Engineering,2016,48(2):98-101.
    [6]
    郜亚松,张步勤,郎利影. 基于深度学习的煤矸石识别技术与实现[J]. 煤炭科学技术,2021,49(12):202-208.

    GAO Yasong,ZHANG Buqin,LANG Liying. Coal and gangue recognition technology and implementation based on deep learning[J]. Coal Science and Technology,2021,49(12):202-208.
    [7]
    ZHOU Hao, ZHANG Jun, LEI Jun, et al. Image semantic segmentation based on FCN-CRF model[C]//International Conference on Image, Vision and Computing, Portsmouth, 2016: 9-14
    [8]
    ZHAO Hengshuang, SHI Jianping, QI Xiaojuan, et al. Pyramid scene parsing network[C]// IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2016.
    [9]
    BADRINARAYANAN V,KENDALL A,CIPOLLA R. SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(12):2481-2495.
    [10]
    RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[M]//NAVAB N, HORNEGGER J, WELLS W M, et al. Medical image computing and computer-assisted intervention, Springer, Cham, 2015: 234-241.
    [11]
    王征,张赫林,李冬艳. 特征压缩激活作用下U−Net网络的煤尘颗粒特征提取[J]. 煤炭学报,2021,46(9):3056-3065.

    WANG Zheng,ZHANG Helin,LI Dongyan. Feature extraction of coal dust particles based on U-Net combined with squeeze and excitation module[J]. Journal of China Coal Society,2021,46(9):3056-3065.
    [12]
    CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[EB/OL]. （2018-08-22） [2021-11-20]. https://arxiv.org/abs/1802.02611.
    [13]
    HOU Qibin, ZHOU Daquan, FENG Jiashi. Coordinate attention for efficient mobile network design[EB/OL]. （2021-03-04） [2021-11-20]. https://arxiv.org/abs/2103.02907.
    [14]
    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. DOI: 10.1109/TPAMI.2017.2699184
    [15]
    翁和王,叶球孙. 图像处理中特征提取的应用及增强算法研究[J]. 重庆理工大学学报(自然科学),2016,30(7):119-122.

    WENG Hewang,YE Qiusun. Applications of feature extraction and enhancement algorithm in image processing[J]. Journal of Chongqing University of Technology(Natural Science),2016,30(7):119-122.
    [16]
    常雪昕,韩军,廖子豪. 基于语义分割的输电线路螺丝识别的研究与实现[J]. 工业控制计算机,2019,32(8):118-120. DOI: 10.3969/j.issn.1001-182X.2019.08.046

    CHANG Xuexin,HAN Jun,LIAO Zihao. Transmission line screw recognition based on semantic segmentation[J]. Industrial Control Computer,2019,32(8):118-120. DOI: 10.3969/j.issn.1001-182X.2019.08.046
    [17]
    屈航, 嵇启春, 段中兴. 改进Deeplab V3+网络在视觉SLAM三维地图构建应用[J/OL]. 小型微型计算机系统: 1-6[2021-12-07]. http://kns.cnki.net/kcms/detail/21.1106.TP.20210818.1048.026.html.

    QU Hang, JI Qichun, DUAN Zhongxing. Improved Deeplab V3+ network application for visual SLAM 3D map construction[J/OL]. Journal of Chinese Computer Systems: 1-6[2021-12-07]. http://kns.cnki.net/kcms/detail/21.1106.TP.20210818.1048.026.html.
    • Related Articles

  • Related Articles

    [1]CHENG Deqiang, ZHENG Lijuan, LIU Jingjing, KOU Qiqi, JIANG He. Quantitative analysis of coal particle size based on bi-level routing attention mechanism[J]. Journal of Mine Automation, 2024, 50(2): 9-17. DOI: 10.13272/j.issn.1671-251x.2023100002
    [2]CAO Xiangang, LI Hu, WANG Peng, WU Xudong, XIANG Jingfang, DING Wentao. A coal foreign object detection method based on cross modal attention fusion[J]. Journal of Mine Automation, 2024, 50(1): 57-65. DOI: 10.13272/j.issn.1671-251x.2023110035
    [3]LI Zhongfei, FENG Shiyong, GUO Jun, ZHANG Yunhe, XU Feixiang. Lightweight safety helmet wearing detection fusing coordinate attention and multiscale feature[J]. Journal of Mine Automation, 2023, 49(11): 151-159. DOI: 10.13272/j.issn.1671-251x.2023080123
    [4]ZHU Fuwen, HOU Zhihui, LI Mingzhen. Lightweight multi-scale cross channel attention coal flow detection network[J]. Journal of Mine Automation, 2023, 49(8): 100-105. DOI: 10.13272/j.issn.1671-251x.2023030045
    [5]YE Ou, DOU Xiaoyi, FU Yan, DENG Jun. Coal block detection method integrating lightweight network and dual attention mechanism[J]. Journal of Mine Automation, 2021, 47(12): 75-80. DOI: 10.13272/j.issn.1671-251x.2021030075
    [6]FANG Xupeng, LI Hui, ZHUANG Jianwei, CHEN Zhiqiao. An identification method for UPS rapid switching[J]. Journal of Mine Automation, 2016, 42(8): 65-69. DOI: 10.13272/j.issn.1671-251x.2016.08.016
    [7]QIN Wei. Design of Dormancy and Wake-up Device Based on MSP430[J]. Journal of Mine Automation, 2011, 37(7): 82-84.
    [8]HU Wan-li, ZHANG Xiu-tai. Design of Sampling System of As-received Coal from Truck Based on PLC[J]. Journal of Mine Automation, 2010, 36(4): 101-103.
    [9]ZHANG Xing-hua, LIU Yu-ying, FENG Xiao-long, WANG Hong-jian, JIANG Hai-ming. Design of Intrinsically Safe and Digitized UPS[J]. Journal of Mine Automation, 2008, 34(5): 72-74.
    [10]LI Lang-guang~(, 2), WANG Cong~. A New Type of UPS Circuit Structure with Dual-inverter and Its Control Strategy[J]. Journal of Mine Automation, 2008, 34(2): 35-38.

  • Cited by

    Periodical cited type(4)

    1. 汤维杰，方挺，韩家明，袁东祥. 基于轻量级MobileNetV2-DeeplabV3+的棒材分割方法. 重庆工商大学学报(自然科学版). 2024(03): 66-71 .
    2. 刘海强，高业成，陈晓晶，葛广建. 基于改进YOLOv7的煤矿输送带异物识别算法. 仪表技术与传感器. 2024(10): 95-99 .
    3. 季亮. 基于改进SOLOv2的煤矿图像实例分割方法. 工矿自动化. 2023(11): 115-120 . 本站查看
    4. 张小国，丁立早，刘亚飞，郑子豪，王庆. 基于双注意力模块的FDA-DeepLab语义分割网络. 东南大学学报(自然科学版). 2022(06): 1145-1151 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (297) PDF downloads (50) Cited by(7)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1342454    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return