Laser & Optoelectronics Progress, Volume. 61, Issue 4, 0437005(2024)

Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar

Yan Wang, Jichuan Xing*, and Yaozhi Wang
Author Affiliations
  • School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
  • show less
    References(30)

    [1] Xie H P, Wu L X, Zheng D Z. Prediction on the energy consumption and coal demand of China in 2025[J]. Journal of China Coal Society, 44, 1949-1960(2019).

    [2] Yang J W. Measures to improve recovery rate of coal resources[J]. Energy and Energy Conservation, 25-27(2022).

    [3] Leonard R, Zulfikar R, Stansbury R. Coal mining and lung disease in the 21st century[J]. Current Opinion in Pulmonary Medicine, 26, 135-141(2020).

    [4] Sun Z Y, Lu W H, Xuan P C et al. Separation of gangue from coal based on supplementary texture by morphology[J]. International Journal of Coal Preparation and Utilization, 42, 221-237(2022).

    [5] Zhang H T, Gong Z Q, Wang Z B et al. Study on combustion and pollutant emission of Shenmu raw coal and its pyrolytic semi-coke[J]. Chemical Reaction Engineering and Technology, 36, 183-192(2020).

    [6] Su L L, Cao X G, Ma H W et al. Research on coal gangue identification by using convolutional neural network[C], 810-814(2018).

    [7] Li D J, Zhang Z X, Xu Z H et al. An image-based hierarchical deep learning framework for coal and gangue detection[J]. IEEE Access, 7, 184686-184699(2019).

    [8] Pu Y Y, Apel D B, Szmigiel A et al. Image recognition of coal and coal gangue using a convolutional neural network and transfer learning[J]. Energies, 12, 1735(2019).

    [9] Paranhos R S, dos Santos E G, Veras M M et al. Performance analysis of optical and X-Ray transmitter sensors for limestone classification in the South of Brazil[J]. Journal of Materials Research and Technology, 9, 1305-1313(2020).

    [10] Robben , Wotruba . Sensor-based ore sorting technology in mining: past, present and future[J]. Minerals, 9, 523(2019).

    [11] Zhang N B, Liu C Y. Radiation characteristics of natural gamma-ray from coal and gangue for recognition in top coal caving[J]. Scientific Reports, 8, 1-9(2018).

    [12] Ma X M, Jiang Y. Digital image processing method of coal gangues[J]. Colliery Mechanical & Electrical Technology, 9-11(2004).

    [13] Hou F. Thorough introduction of LIDAR and overview of its application[J]. Science Mosaic, 95-100(2014).

    [14] Gong W, Shi S, Chen B W et al. Development and application of airborne hyperspectral LiDAR imaging technology[J]. Acta Optica Sinica, 42, 1200002(2022).

    [15] Song J H, Han S H, Yu K Y et al. Assessing the possibility of land-cover classification using lidar intensity data[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34, 259-262(2002).

    [16] Liu X Y, Zhang Z Y, Peterson J et al. LiDAR-derived high quality ground control information and DEM for image orthorectification[J]. GeoInformatica, 11, 37-53(2007).

    [17] Zhao Z M. Intelligent separation technology of coal and gangue based on LiDAR imaging[D](2021).

    [18] Su L L, Cao X G, Ma H W et al. Research on coal gangue identification by using convolutional neural network[C], 810-814(2018).

    [19] Li M, Duan Y, Cao X G et al. Image identification method and system for coal and gangue sorting robot[J]. Journal of China Coal Society, 45, 3636-3644(2020).

    [20] Li P X, Liu H B. Research on coal gangue recognition system based on convolutional neural network[J]. Automation Application, 8-14, 19(2021).

    [22] Hong L, Gao S, Li X. Layer-wise pruning method based on network characteristics[J]. Journal of Jilin University (Science Edition), 60, 1407-1415(2022).

    [23] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [24] Zhang C P. Research on some key problems of recurrent neural networks[D](2021).

    [25] Xu K S, Wang H L, Tang P J. Image captioning with deep LSTM based on sequential residual[C], 361-366(2017).

    [26] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [27] Xing J C, Zhao Z M, Wang Y Z et al. Coal and gangue identification method based on the intensity image of lidar and DenseNet[J]. Applied Optics, 60, 6566-6572(2021).

    [28] Goutte C, Gaussier E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation[M]. Losada D E, Fernández-Luna J M. Advances in Information Retrieval. Lecture notes in computer science, 3408, 345-359(2005).

    [29] Song W H. Research and implementation of coal and gangue identification based on deep learning[D](2021).

    [30] Xu Z Q, Lü Z Q, Wang W D et al. Machine vision recognition method and optimization for intelligent separation of coal and gangue[J]. Journal of China Coal Society, 45, 2207-2216(2020).

    Tools

    Get Citation

    Copy Citation Text

    Yan Wang, Jichuan Xing, Yaozhi Wang. Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Digital Image Processing

    Received: Dec. 1, 2022

    Accepted: Feb. 6, 2023

    Published Online: Feb. 26, 2024

    The Author Email: Xing Jichuan (michaelhsing@bit.edu.cn)

    DOI:10.3788/LOP223222

    Topics