Volume 48 Issue 1
Jan.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(1): 53-57
TANG Shoufeng, SHI Jingcan, ZHOU Nan, et al. Digital recognition method of methane sensor based on improved CNN-SVM[J]. Industry and Mine Automation，2022，48(1)：52-56.  doi: 10.13272/j.issn.1671-251x.2021070033
Citation: TANG Shoufeng, SHI Jingcan, ZHOU Nan, et al. Digital recognition method of methane sensor based on improved CNN-SVM[J]. Industry and Mine Automation，2022，48(1)：52-56.  doi: 10.13272/j.issn.1671-251x.2021070033
shu

Digital recognition method of methane sensor based on improved CNN-SVM

doi: 10.13272/j.issn.1671-251x.2021070033

  • School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • Received Date: 2021-07-13
  • Rev Recd Date: 2022-01-09
  • Publish Date: 2022-01-20
    Abstract
  • The methane sensor material has light reflection, and there are attachments on the display panel, which causes the poor quality of the sensor numerical image collected by the automatic verification system of methane sensor, and the difficulty of character recognition. However, the existing instrument character recognition methods based on machine learning have low recognition rate and slow algorithm running speed. In order to solve the above problems, a digital recognition method of methane sensor based on improved convolutional neural network (CNN) and support vector machine (SVM) is proposed. The numerical image of methane sensor is preprocessed by four steps, including image enhancement, numerical region image extraction, image segmentation and decimal point positioning. And the processed digital images are taken as a custom data set. In order to solve the problem of long running time of the CNN-SVM model, PCA algorithm is used to reduce the dimension of the image characteristics extracted from the CNN fully connected layer, and the most important data characteristics are used to replace the original data as the samples of the SVM classifier for classification and recognition. The verification results on the custom dataset show that the improved CNN-SVM model has higher accuracy and shorter running time than the traditional CNN model and CNN-SVM model. The verification results on the classical MNIST dataset show that considering the precision and real-time requirements, the improved CNN-SVM model has better comprehensive performance than CRNN, SSD, YOLOv3 and Faster R-CNN. A micro high-definition USB camera is used to collect the numerical images of methane sensor. The trained improved CNN-SVM model is transplanted to raspberry pi for image processing and recognition. The results show that the recognition success rate of methane sensor digital recognition method based on improved CNN-SVM is 99%, which is consistent with the simulation analysis results.

     

    • FullText(HTML)
  • loading
    • References(13)
  • [1]
    陈英, 李磊, 汪文源, 等. 家用水表字符的识别算法研究[J]. 现代电子技术,2018,41(18):99-103.

    CHEN Ying, LI Lei, WANG Wenyuan, et al. Research on character recognition algorithm for domestic water meter[J]. Modern Electronics Technique,2018,41(18):99-103.
    [2]
    潘帅成, 韩磊, 陶毅, 等. 基于卷积神经网络的水表字符识别方法研究[J]. 计算机时代,2020(2):25-28.

    PAN Shuaicheng, HAN Lei, TAO Yi, et al. Research on character recognition technology for watermeter based on deep convolution neural network[J]. Computer Era,2020(2):25-28.
    [3]
    肖佳. 基于机器视觉的数字仪表自动读数方法研究[D]. 重庆: 重庆大学, 2017.

    XIAO Jia. Study on automatic reading method of digital instrument based on machine vision[D]. Chongqing: Chongqing University, 2017.
    [4]
    CALEFATI A, GALLO I, NAWAZ S. Reading meter numbers in the wild[C]//Digital Image Computing: Techniques and Applications, Perth, 2019: 1-6.
    [5]
    高晓利, 李捷, 王维, 等. 基于CRNN的汽车发动机声纹个体识别方法[J]. 火力与指挥控制,2021,46(3):150-153. doi: 10.3969/j.issn.1002-0640.2021.03.025

    GAO Xiaoli, LI Jie, WANG Wei, et al. Individual identification method of automobile engine voiceprint based on CRNN[J]. Fire Control & Command Control,2021,46(3):150-153. doi: 10.3969/j.issn.1002-0640.2021.03.025
    [6]
    SAI K M, CHANDRIKA P H, BEBE K, et al. Optical character recognition using CRNN[J]. International Journal of Innovative Technology and Exploring Engineering,2020,9(8):115-120. doi: 10.35940/ijitee.H6264.069820
    [7]
    LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, 2016: 21-37.
    [8]
    REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
    [9]
    LIN Xiaoping, DUAN Peiyong, ZHENG Yuanjie, et al. Posting techniques in indoor environments based on deep learning for intelligent building lighting system[J]. IEEE Access,2020,8:13674-13682. doi: 10.1109/ACCESS.2019.2959667
    [10]
    孟彩茹, 宋京, 孙明扬. 基于改进CNN与SVM的手势识别研究[J]. 现代电子技术,2020,43(22):128-131.

    MENG Cairu, SONG Jing, SUN Mingyang. Research on gesture recognition based on improved CNN and SVM[J]. Modern Electronics Technique,2020,43(22):128-131.
    [11]
    黄洁. 非色散红外甲烷传感器自动检定系统研究[D]. 徐州: 中国矿业大学, 2020.

    HUANG Jie. Research on automatic verification system of non-dispersive infrared methane sensor[D]. Xuzhou: China University of Mining and Technology, 2020.
    [12]
    林仁耀, 邓浩伟, 兰红. 卷积神经网络结合SVM的手写数字识别算法[J]. 通信技术,2019,52(10):2389-2394. doi: 10.3969/j.issn.1002-0802.2019.10.012

    LIN Renyao, DENG Haowei, LAN Hong. Handwritten digits recognition algorithm based on convolutional neural network and SVM[J]. Communications Technology,2019,52(10):2389-2394. doi: 10.3969/j.issn.1002-0802.2019.10.012
    [13]
    刘昶, 徐超远, 张鑫, 等. 液晶字符识别的CNN和SVM组合分类器[J]. 图学学报,2021,42(1):15-22.

    LIU Chang, XU Chaoyuan, ZHANG Xin, et al. A combined classifier based on CNN and SVM for LCD character recognition[J]. Journal of Graphics,2021,42(1):15-22.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (128) PDF downloads(21) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return