Multi-classifier Combined Anomaly Detection Algorithm Based On Feature Map In Underground Coal Mine

The detection of abnormal activities in deep learning is of great significance for preventing the occurrence of abnormal disasters in mine production. As the underground scenes of coal mines are characterized by much noise and uneven light, the traditional manual feature extraction method has little...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-04, Vol.1894 (1), p.12099
Main Authors: Fu, Yan, Cui, Zemin, Ye, Ou
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Classifiers
Coal mines
Coal mining
Datasets
Feature extraction
Feature maps
Graph matching
Machine learning
Physics
Underground mines
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Summary:The detection of abnormal activities in deep learning is of great significance for preventing the occurrence of abnormal disasters in mine production. As the underground scenes of coal mines are characterized by much noise and uneven light, the traditional manual feature extraction method has little obvious effect in the underground and low accuracy of anomaly detection. To solve the above problems, a feature extraction method combining CNN+LSTM is proposed. Secondly, the obtained features are matched by graph structure. Finally, multiple classifiers are used to classify the features before and after matching. In this paper, experiments are carried out in coal mine dataset and UCSDped1 dataset respectively, and comparisons are made with some classical algorithms. Experimental show that the algorithm achieves high recognition accuracy in different abnormal event datasets.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1894/1/012099