Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism

Tianwei Zheng; Yuancheng Li; Chi Liu; Pai Wang; Beizhan Liu; Xuebin Qin; Mei Wang; Yuan Guo

doi:10.1109/IS3C50286.2020.00139

Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism

Tianwei Zheng
, Yuancheng Li
, Chi Liu
, Pai Wang
, Beizhan Liu
, Xuebin Qin
, Mei Wang^*
, Yuan Guo

^*Corresponding author for this work

School of Computer Science and Technology

Xi'an University of Science and Technology
Ltd.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063%.

Original language	English
Title of host publication	Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	513-516
Number of pages	4
ISBN (Electronic)	9781728193625
DOIs	https://doi.org/10.1109/IS3C50286.2020.00139
Publication status	Published - Nov 2020
Event	2020 International Symposium on Computer, Consumer and Control, IS3C 2020 - Taichung, Taiwan, Province of China Duration: 13 Nov 2020 → 16 Nov 2020

Publication series

Name	Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Conference

Conference	2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Country/Territory	Taiwan, Province of China
City	Taichung
Period	13/11/20 → 16/11/20

Keywords

CNN-LSTM
Data enhancement
Scene recognition
Spatial-temporal attention mechanism

Access to Document

10.1109/IS3C50286.2020.00139

Cite this

Zheng, T., Li, Y., Liu, C., Wang, P., Liu, B., Qin, X., Wang, M., & Guo, Y. (2020). Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism. In Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020 (pp. 513-516). Article 9394028 (Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IS3C50286.2020.00139

Zheng, Tianwei ; Li, Yuancheng ; Liu, Chi et al. / Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism. Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 513-516 (Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020).

@inproceedings{fb31c1596eea45c9a48b9a2743e0edb8,

title = "Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism",

abstract = "Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136\%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921\%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063\%.",

keywords = "CNN-LSTM, Data enhancement, Scene recognition, Spatial-temporal attention mechanism",

author = "Tianwei Zheng and Yuancheng Li and Chi Liu and Pai Wang and Beizhan Liu and Xuebin Qin and Mei Wang and Yuan Guo",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 2020 International Symposium on Computer, Consumer and Control, IS3C 2020 ; Conference date: 13-11-2020 Through 16-11-2020",

year = "2020",

month = nov,

doi = "10.1109/IS3C50286.2020.00139",

language = "English",

series = "Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "513--516",

booktitle = "Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020",

address = "United States",

}

Zheng, T, Li, Y, Liu, C, Wang, P, Liu, B, Qin, X, Wang, M & Guo, Y 2020, Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism. in Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020., 9394028, Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020, Institute of Electrical and Electronics Engineers Inc., pp. 513-516, 2020 International Symposium on Computer, Consumer and Control, IS3C 2020, Taichung, Taiwan, Province of China, 13/11/20. https://doi.org/10.1109/IS3C50286.2020.00139

Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism. / Zheng, Tianwei; Li, Yuancheng; Liu, Chi et al.
Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 513-516 9394028 (Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism

AU - Zheng, Tianwei

AU - Li, Yuancheng

AU - Liu, Chi

AU - Wang, Pai

AU - Liu, Beizhan

AU - Qin, Xuebin

AU - Wang, Mei

AU - Guo, Yuan

PY - 2020/11

Y1 - 2020/11

N2 - Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063%.

AB - Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063%.

KW - CNN-LSTM

KW - Data enhancement

KW - Scene recognition

KW - Spatial-temporal attention mechanism

UR - https://www.scopus.com/pages/publications/85104893219

U2 - 10.1109/IS3C50286.2020.00139

DO - 10.1109/IS3C50286.2020.00139

M3 - Conference contribution

AN - SCOPUS:85104893219

T3 - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

SP - 513

EP - 516

BT - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Y2 - 13 November 2020 through 16 November 2020

ER -

Zheng T, Li Y, Liu C, Wang P, Liu B, Qin X et al. Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism. In Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 513-516. 9394028. (Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020). doi: 10.1109/IS3C50286.2020.00139

Scene recognition model in underground mines based on CNN-LSTM and spatial-temporal attention mechanism

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