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

^a* Corresponding author: Jichao.L19@cug.edu.cn
^b e-mail: guoshy@cug.edu.cn
^c e-mail: kongliulin@cug.edu.cn
^d e-mail: tansiqi@cug.edu.cn
^e e-mail: 20181002062@cug.edu.cn

Abstract

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.