E3S Web Conf.
Volume 253, 20212021 International Conference on Environmental and Engineering Management (EEM 2021)
|Number of page(s)||4|
|Section||Environmental Equipment Engineering Management and its Technical Application|
|Published online||06 May 2021|
An improved YOLOv3-tiny method for fire detection in the construction industry
1 School of Economics and Management China University of Geosciences, CUG Wuhan, China
2 School of Economics and Management China University of Geosciences, CUG Wuhan, China
3 School of Engineering China University of Geosciences, CUG Wuhan, China
4 School of Economics and Management China University of Geosciences, CUG Wuhan, China
5 School of Economics and Management China University of Geosciences, CUG Wuhan, China
To prevent fire accidents on construction site and improve the accuracy of fire detection, an improved YOLOv3-tiny method (I-YOLOv3-tiny) is proposed in this paper. Although the YOLOv3-tiny has a fast detection speed and low equipment requirement, the accuracy is relatively low on fire detection. The improvement of the I-YOLOv3-tiny method is followed by three steps. Firstly, the feature extraction of fire images is enhanced by optimizing the network structure. Secondly, a multi-scale fusion is used to improve the detection effect of fire targets. Finally, the anchor boxes that are suitable for fire data sets are selected by k-means clustering. The results show that I-YOLOv3-tiny has an increased percentage of 4 on the mAP, the Recall rate has an increased percentage of 4, and AVG IOU has an increased percentage of 6. The proposed model meets the real-time performance of fire detection. This study is of theoretical and practical significance on fire safety management and accident prevention in the construction industry.
© The Authors, published by EDP Sciences, 2021
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