Coal and Rock Classification with Rib Images and Machine Learning Techniques

被引：0
|
作者：
Yuting Xue
机构：
[1] CDC NIOSH Pittsburgh Mining Research Division,
来源：
Mining, Metallurgy & Exploration | 2022年 / 39卷
关键词：
Rock classification; Image processing; Patch; Machine learning; SVM; Random forest;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Classification of rock and coal is one preliminary problem for fully automated or intelligent mining. It assists for the automated rib stability analysis and enables the shearer to adjust the drums without human intervention. In this paper, the classification of rock from coal on rib images has been studied with machine learning techniques. A database of rock and coal image has been created by filtering photographs taken by NIOSH researchers in gateroad during site visits and only the images with fresh areas of rock and coal on the rib were selected. Machine learning was conducted on patches with a determined size, which are smaller images randomly extracted from each rock or coal image. After training, the classifier was validated with the testing dataset and an accuracy score of 0.9 was obtained. The influence of patch size and classifier was also investigated. The trained classifier was then applied to classify rock and coal on a new rib image with three rock layers of different thicknesses and good agreement was achieved.
引用
收藏
页码：453 / 465
页数：12
相关论文
共 50 条
  • [21] Machine learning and spectral techniques for lithological classification
    Parakh, Khushboo
    Thakur, Sanchari
    Chudasama, Bijal
    Tirodkar, Siddhesh
    Porwal, Alok
    Bhattacharya, Avik
    MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL REMOTE SENSING TECHNOLOGY, TECHNIQUES AND APPLICATIONS VI, 2016, 9880
  • [22] Machine Learning Techniques for Anomalies Detection and Classification
    Abdel-Aziz, Amira Sayed
    Hassanien, Aboul Ella
    Azar, Ahmad Taher
    Hanafi, Sanaa El-Ola
    ADVANCES IN SECURITY OF INFORMATION AND COMMUNICATION NETWORKS, 2013, 381 : 219 - +
  • [23] Supervised machine learning: A review of classification techniques
    Department of Computer Science and Technology, University of Peloponnese, End of Karaiskaki, 22100 , Tripolis GR, Greece
    Inf, 2007, 3 (249-268):
  • [24] Supervised Machine Learning: A Review of Classification Techniques
    Kotsiantis, S. B.
    INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS, 2007, 31 (03): : 249 - 268
  • [25] Machine Learning Techniques for Classification of Breast Cancer
    Osmanovic, Ahmed
    Halilovic, Sabina
    Ilah, Layla Abdel
    Fojnica, Adnan
    Gromilic, Zehra
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1, 2019, 68 (01): : 197 - 200
  • [26] CLASSIFICATION OF ECG ARRHYTHMIA WITH MACHINE LEARNING TECHNIQUES
    Bulbul, Halil Ibrahim
    Usta, Nese
    Yildiz, Musa
    2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, : 546 - 549
  • [27] Frog classification using machine learning techniques
    Huang, Chenn-Jung
    Yang, Yi-Ju
    Yang, Dian-Xiu
    Chen, You-Jia
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) : 3737 - 3743
  • [28] Exploration of Machine Learning Techniques for Defect Classification
    Prakash, B. V. Ajay
    Ashoka, D. V.
    Aradya, V. N. Manjunath
    COMPUTING AND NETWORK SUSTAINABILITY, 2017, 12 : 145 - 153
  • [29] Microalgae classification based on machine learning techniques
    Otalora, P.
    Guzman, J. L.
    Acien, F. G.
    Berenguel, M.
    Reul, A.
    ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS, 2021, 55
  • [30] Machine learning techniques for classification of breast tissue
    Helwan, Abdulkader
    Idoko, John Bush
    Abiyev, Rahib H.
    9TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTION, ICSCCW 2017, 2017, 120 : 402 - 410
12345