Citation

Shulin Z, Ya'nan Z, Chao T, Xiang YN, Yi LU, Shiliang SHI. China Saf. Sci. J. 2024; 34(1): 179-186.

Copyright

(Copyright © 2024, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2024.01.2333

PMID

unavailable

Abstract

In order to discover fire accidents in chemical industrial parks in time and reduce accident losses, this study used CNN to establish a real-time fire detection system for chemical industrial parks. Based on CNN, the YOLOv5 algorithm was used to calculate chemical industrial park fire data sets and ordinary fire data sets. The loss value, recall rate, precision and mean average precision of the two data sets were compared. Among them, the loss value and recall rate of the chemical industrial park fire data set are slightly lower, but the precision and mean average precision were higher than that of an ordinary fire data set, which shows the feasibility of detecting fire. In addition, based on fire detection results, this study further designed the flame image recognition software system for the chemical industry park with the help of the PyQt5 program framework, realized the application of fire image and video recognition in the chemical park, and expanded the application scope of the method. The results show that the YOLOv5 target detection algorithm based on convolutional neural network can detect fires in chemical industrial parks in real-time. This detection method is portable,and the results are reliable, which can help improving the safety management level of the chemical industrial park.

Key words: chemical industrial park, fire flame, image recognition, convolutional neural networks, YOLOv5 algorithm, fire data set

CLC Number:

X932爆炸安全与防火、防爆

Language: en