|Table of Contents|

Flame Detection Based on Faster R-CNN Model(PDF)

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

Issue:
2018年03期
Page:
1-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Flame Detection Based on Faster R-CNN Model
Author(s):
Yan Yunyang1; 2; Zhu Xiaoyu1; 2; Liu Yi’an2; Gao Shangbing1
(1.Faculty of Computer & Software Engineering,Huaiyin Institute of Technology,Huaian 223003,China)(2.School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)
Keywords:
Faster R-CNN; RPN; Fast R-CNN; flame detection
PACS:
TP391.41
DOI:
10.3969/j.issn.1001-4616.2018.03.001
Abstract:
Usually the static or dynamic characteristics of the flame is extracted for flame detection. But the traditional characteristics can not fully describe the characteristics of flame,which leads to the reduction of recognition accuracy. To solve this problem,a flame detection based on Faster R-CNN model is proposed in this paper. First,the candidate region of the flame is extracted by RPN. Then the convolution and pool operation of candidate regions are performed to extract the flame characteristics. Finally,Fast R-CNN is used to identify the flame. The experimental results show that the method can automatically extract the flame characteristics,effectively improve the accuracy of flame recognition in the complex background,and have good generalization ability and robustness.

References:

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Last Update: 2018-11-19