Citation

Wang Z, Li X, Yang D, Liu D. China Saf. Sci. J. 2023; 33(6): 152-158.

Copyright

(Copyright © 2023, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2023.06.1532

PMID

unavailable

Abstract

In order to accurately monitor the fires at wildland-urban interfaces and locate their spatial distribution, a target detection model of wildland-urban interface fires based on the improved YOLOv5s network was proposed. Images of fires at the wildland-urban interfaces were collected, and object detection datasets were annotated with the image annotation tool. CA mechanism was introduced into the backbone network of YOLOv5s to enhance the orientation and location information perception of the model to accurately locate the fire point at the wildland-urban interface. Based on the evaluation indicators of accuracy, recall rate and average accuracy, training and testing were carried out on the self-built data set. The experimental results show that the overall performance of the improved YOLOv5s model is improved, and the average accuracy of building fires increases by 0. 8% and forest fires by 1. 3% in the detection of fire targets in the wildland-urban interface. ©2023 Journal of Northwestern Polytechnical University.

Language: zh