Integrative binocular vision detection method based on infrared and visible light fusion for conveyor belts longitudinal tear

被引：50
作者：
Qiao, Tiezhu [1 ]
Chen, Lulu [1 ]
Pang, Yusong [1 ,2 ]
Yan, Gaowei [1 ]
Miao, Changyun [3 ]
机构：
[1] Taiyuan Univ Technol, Engn Res Ctr Measuring & Controlling Technol & Ad, Taiyuan 030024, Shanxi, Peoples R China
[2] Delft Univ Technol, Fac Mech, Sch Transport Engn & Logist, Delft, Netherlands
[3] Tianjin Polytech Univ, Sch Elect & Informat Engn, Tianjin 300387, Peoples R China
来源：
MEASUREMENT | 2017年 / 110卷
基金：
中国国家自然科学基金;
关键词：
Longitudinal tear detection; Integrative binocular vision detection (IBVD); Infrared and visible light fusion; Projection method; MONOCULAR VISION; COMPUTER VISION; IMAGE; SEGMENTATION; TRANSFORM; SYSTEM;
D O I：
10.1016/j.measurement.2017.06.032
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Real-time and reliable detection of conveyor belt longitudinal tear is an important task in mining operations. This paper presents a novel method of Integrative Binocular Vision Detection (IBVD) to detect the longitudinal tears of conveyor belts. Based on infrared and visible fusion, the IBVD sensor device collects the fusion images of the belt. After extracting the tear features by projection method, the progress of potential tears can be evaluated and the tears can be identified. The IBVD method is verified by an experiment platform fulfilling the acquisition, pro-processing and analysis of fusion images for tear detection. The fusion image processing time is less than 18 ms, which satisfies the requirement of real-time online monitoring. Compared to the individual measurement technique of either infrared detection or visible light detection, the average accuracy of the IBVD method reaches up to 96%, the IBVD method is more reliable in tear detection. (c) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：192 / 201
页数：10
相关论文
共 24 条
  • [1] Image fusion based on nonsubsampled contourlet transform for infrared and visible light image
    Adu, Jianhua
    Gan, Jianhong
    Wang, Yan
    Huang, Jian
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2013, 61 : 94 - 100
  • [2] Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform
    Bai, Xiangzhi
    Zhou, Fugen
    Xue, Bindang
    [J]. OPTICS EXPRESS, 2011, 19 (09): : 8444 - 8457
  • [3] Fuzzy mathematical morphology for biological image segmentation
    Caponetti, Laura
    Castellano, Giovanna
    Basile, M. Teresa
    Corsini, Vito
    [J]. APPLIED INTELLIGENCE, 2014, 41 (01) : 117 - 127
  • [4] Chen H. T., 2015, IEEE SENS J, V16, P1
  • [5] The segmentation of timber defects based on color and the mathematical morphology
    Chen, LiJun
    Wang, KeQi
    Xie, YongHua
    Wang, JinCong
    [J]. OPTIK, 2014, 125 (03): : 965 - 967
  • [6] Fromme C. C., 2006, LIS Patent, Patent No. 6988610
  • [7] Green operations of belt conveyors by means of speed control
    He, Daijie
    Pang, Yusong
    Lodewijks, Gabriel
    [J]. APPLIED ENERGY, 2017, 188 : 330 - 341
  • [8] Real-time obstacle detection with motion features using monocular vision
    Jia, Baozhi
    Liu, Rui
    Zhu, Ming
    [J]. VISUAL COMPUTER, 2015, 31 (03) : 281 - 293
  • [9] Fire detection based on vision sensor and support vector machines
    Ko, Byoung Chul
    Cheong, Kwang-Ho
    Nam, Jae-Yeal
    [J]. FIRE SAFETY JOURNAL, 2009, 44 (03) : 322 - 329
  • [10] Fire detection - A new approach based on a low cost CCD camera in the near infrared
    Le Maoult, Y.
    Sentenac, T.
    Orteu, J. J.
    Arcens, J. P.
    [J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2007, 85 (B3) : 193 - 206
123