Citation

Zhao X, Zhang Q, Wang W, Xu Z. China Saf. Sci. J. 2020; 30(4): 8-13.

Copyright

(Copyright © 2020, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2020.04.002

PMID

unavailable

Abstract

In recent years, production accidents caused by dust explosion occur frequently, and on-line detection and early warning of dust cloud concentration in dust gathering places has become a key means to control dust explosion. However, installation and identification of dust concentration sensors were limited in large space where dust cloud gathers. In order to address this, combustible dust cloud recognition method based on deep learning was proposed. End-to-end detection and identification of explosive dust cloud were conducted by using CNN-based Faster R-CNN model. Then, a standard concentration image database was established to verify experimental results. The results show that Faster R-CNN model can effectively detect and identify explosive dust clouds, and it has high recognition accuracy. © 2019 China Safety Science Journal

Language: zh