Fire Detection Model Based on Multi-scale Feature Fusion
[1]Zhang Jianxin,Guo Si Jing,Zhang Guolan,et al.Fire Detection Model Based on Multi-scale Feature Fusion[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):13-18.[doi:10.13705/j.issn.1671-6833.2021.05.016]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
42卷
Number of periods:
2021 05
Page number:
13-18
Column:
Public date:
2021-09-10
- Title:
- Fire Detection Model Based on Multi-scale Feature Fusion
- Keywords:
- deep learning; fire detection; convolutional neural network; multi-scale features; feature pyramid network
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2021.05.016
- Abstract:
- This paper aims to modify the two-scene detection model Faster R-CNN. Specifically, this model uses Resnet101 to extract features which are processed by pyramid structure FPN to extract the shallow and high-level features of Resnet101. The shallow feature map of Resnet101 is input into Inception Module structure to obtain the convolutional features of multiple sizes, and finally the proposed model uses the pixel attention mechanism and channel attention mechanism to emphasize the target position and weaken the rest, which makes the detection target more accurate. This network avoids the problem of insufficient feature extraction of trunk network, and integrates features of various scales to distinguish fire area and non-fire area, thus effectively improves the detection accuracy of fire image data sets, and mean average precision MAP is 0.851.
- References:
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Last Update:
2021-10-11