基于深度学习的火焰分割模型对比研究

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (1): 25-30.

基于深度学习的火焰分割模型对比研究

朱红，王海雷，张昊轩，陈鹏

(中国矿业大学（北京），北京100083)

出版日期:2022-01-15 发布日期:2022-01-15
作者简介:朱红（1974-），女，中国矿业大学（北京）机电与信息工程学院副教授，主要从事图形图像处理和人工智能研究，北京市海淀区学院路丁11号矿大逸夫楼822，100083。

Comparative research on flame segmentation models based on deep learning

ZHU Hong, WANG Hai-lei, ZHANG Hao-xuan, CHEN Peng

(China University of Mining and Technology (Beijing), Beijing 100083, China)

Online:2022-01-15 Published:2022-01-15

摘要/Abstract

关键词: 消防；图像处理；深度学习；神经网络；火焰分割

Abstract: Due to the lack of flame segmentation data set, the application of traditional image segmentation methods on flame segmentation study is inadequate, and the model comparison test is not enough. To deal with these problems, based on the construction of the flame segmentation data set, 4 kinds of semantic segmentation models and 2 kinds of backbone networks which perform well in public dataset were chosen for training and testing, and were compared and analyzed under different application scenario. Experimental results showed that, U-Net model has better effect in the flame segmentation, in which U-Net+Resnet50 has the best comprehensive effect, while U-Net+Mobilenet V2 has slightly worse effect, but higher running speed.

Key words: fire protection; image processing; deep learning; neural networks; flame segmentation

朱红，王海雷，张昊轩，陈鹏. 基于深度学习的火焰分割模型对比研究[J]. 消防科学与技术, 2022, 41(1): 25-30.

ZHU Hong, WANG Hai-lei, ZHANG Hao-xuan, CHEN Peng. Comparative research on flame segmentation models based on deep learning[J]. Fire Science and Technology, 2022, 41(1): 25-30.