Open AccessMulti-classifier Combined Anomaly Detection Algorithm Based On Feature Map In Underground Coal Mine
Author(s) -
Yan Fu,
Zemin Cui,
Ou Ye
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1894/1/012099
Subject(s) - coal mining , artificial intelligence , computer science , pattern recognition (psychology) , classifier (uml) , feature extraction , anomaly detection , graph , feature (linguistics) , coal , data mining , engineering , linguistics , philosophy , waste management , theoretical computer science
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.