|Table of Contents|

Video Flame Detection Based on Fusion of Multilevel Features(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年03期
Page:
131-136
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Video Flame Detection Based on Fusion of Multilevel Features
Author(s):
Yan Chen1; Yan Yunyang1; 2; Gao Shangbing1; Zhu Quanyin1
(1.Faculty of Computer & Software Engineering,Huaiyin Institute of Technology,Huai’an 223003,China)(2.School of Computer Engineering,Jiangsu Ocean University,Lianyungang 225005,China)
Keywords:
flame detection; object detection; feature fusion; Yolov2
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.03.019
Abstract:
In the early stage,the flame is usually small target,but the general flame detection method has poor detection ability for small target. To detect early flame and improve fire prevention ability,a video flame detection method with multi-stage features is proposed. Aiming at the problem of missing target caused by the lower sampling resolution,a deconvolution module is introduced,and the features with strong semantic information in the deep layer and strong detail information in the shallow layer are combined to effectively improve the detection rate of flame. The experiment of the proposed algorithm on the Fire database VisiFire data set of Bilkent University showed that mAP improved by 10.0% compared with the Yolov2,and the detection rate was higher compared with many classic deep learning algorithm models.

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Last Update: 2021-09-15