Multispectral visual detection method for conveyor belt longitudinal tear

As an important part of modern coal mine production, conveyor belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine conveyor belt and solve the drawbacks of the existing conveyor belt longitudinal tear detection technology, a multispectral visual detection method for conveyor belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the conveyor belt longitudinal tear, but also accurately classifies and identify other states of the conveyor belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.

[1]  Gabriel Lodewijks,et al.  Healthy speed control of belt conveyors on conveying bulk materials , 2018 .

[2]  Daniela Marasová,et al.  Failure analysis of the rubber-textile conveyor belts using classification models , 2019, Engineering Failure Analysis.

[3]  G. Lodewijks,et al.  A Novel Embedded Conductive Detection System for Intelligent Conveyor Belt Monitoring , 2006, 2006 IEEE International Conference on Service Operations and Logistics, and Informatics.

[4]  Daniela Marasová,et al.  Measurement and simulation of impact wear damage to industrial conveyor belts , 2016 .

[5]  Changyun Miao,et al.  The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm☆ , 2016 .

[6]  Miriam Andrejiová,et al.  Analysis of influence of conveyor belt overhang and cranking on pipe conveyor operational characteristics , 2015 .

[7]  A. Pramanik,et al.  Developments of rubber material wear in conveyer belt system , 2017 .

[8]  Andrea C. Santomaso,et al.  Artificial vision system for the online characterization of the particle size distribution of bulk materials on conveyor belts , 2018 .

[9]  Li Xianguo,et al.  Laser-based on-line machine vision detection for longitudinal rip of conveyor belt , 2018, Optik.

[10]  Bidyut Baran Chaudhuri,et al.  A survey of Hough Transform , 2015, Pattern Recognit..

[11]  Teodor Tóth,et al.  Failure analysis of irreversible changes in the construction of rubber–textile conveyor belt damaged by sharp-edge material impact , 2014 .

[12]  Giulio Rosati,et al.  Real-time defect detection on highly reflective curved surfaces , 2009 .

[13]  Gabriel Fedorko,et al.  Influence of selected characteristics on failures of the conveyor belt cover layer material , 2018, Engineering Failure Analysis.

[14]  Tomasz Kozłowski,et al.  The use of magnetic sensors in monitoring the condition of the core in steel cord conveyor belts – Tests of the measuring probe and the design of the DiagBelt system , 2018, Measurement.

[15]  Changyun Miao,et al.  On-line conveyor belts inspection based on machine vision , 2014 .

[16]  Shahrokh Heidari,et al.  A novel quantum binary images thinning algorithm: A quantum version of the Hilditch's algorithm , 2017 .

[17]  Xian Du,et al.  Hair segmentation using adaptive threshold from edge and branch length measures , 2017, Comput. Biol. Medicine.

[18]  Fanjie Meng,et al.  Image fusion based on object region detection and Non-Subsampled Contourlet Transform , 2017, Comput. Electr. Eng..

[19]  Takeo Kanade,et al.  Computer Vision and Image Understanding Computer Vision for Assistive Technologies , 2022 .

[20]  S. Muttan,et al.  Discrete wavelet transform based principal component averaging fusion for medical images , 2015 .

[21]  Yourui Huang,et al.  Study of multi-agent-based coal mine environmental monitoring system , 2015 .

[22]  Rabab Kreidieh Ward,et al.  Deep learning for pixel-level image fusion: Recent advances and future prospects , 2018, Inf. Fusion.

[23]  C. Bennila Thangammal,et al.  Visible and infrared image fusion using DTCWT and adaptive combined clustered dictionary , 2018, Infrared Physics & Technology.

[24]  Daniela Marasová,et al.  Measurement and determination of the absorbed impact energy for conveyor belts of various structures under impact loading , 2019, Measurement.

[25]  Bernd G. Lottermoser,et al.  The need for sustainable technology diffusion in mining: Achieving the use of belt conveyor systems in the German hard-rock quarrying industry , 2017 .

[26]  Yang Wang,et al.  Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization , 2017 .

[27]  Zunlin Fan,et al.  Infrared image enhancement with learned features , 2017 .

[28]  Kesari Verma,et al.  An Enhancement in Adaptive Median Filter for Edge Preservation , 2015 .

[29]  Bangyong Sun,et al.  Design of four-band multispectral imaging system with one single-sensor , 2018, Future Gener. Comput. Syst..

[30]  Changyun Miao,et al.  Integrative binocular vision detection method based on infrared and visible light fusion for conveyor belts longitudinal tear , 2017 .

[31]  Haitao Zhang,et al.  Dual band infrared detection method based on mid-infrared and long infrared vision for conveyor belts longitudinal tear , 2018 .