期刊文献+

改进DeeplabV3+的火焰分割与火情分析方法 被引量:9

Fire segmentation based on the improved DeeplabV3+and the analytical method for fire development
下载PDF
导出
摘要 火焰检测与火势发展分析对火情的控制十分重要。提出一种改进DeeplabV3+的火焰分割与火情分析方法:首先在DeeplabV3+的解码器部分增加低层特征来源,将其与高层特征融合后采用2倍上采样逐步恢复图像尺寸,保留更多的细节信息,实现更加准确的火焰分割;然后将火焰视频每帧分割得到的像素数组成火势发展序列,基于关键点对序列进行分段和线性拟合,获取火势发展的关键趋势。实验结果表明,所提方法可以在对火焰进行准确分割的基础上,有效地分析火情发展态势,为火情的检测与控制提供有效的帮助。 Fire detection and development analysis are significant for fire control.The fire segmentation based on the improved DeeplabV3+and the analytical method for fire development are proposed:First,the low-level feature sources are added to the decoder of the DeeplabV3+,which is fused with high-level features,and the image size is gradually recovered by 2 times up sampling to retain more details and achieve more accurate fire segmentation.Then,the number of pixels obtained by each fire video frame is combined into a fire series,and key points are used to segment and linearly fit the series to obtain the key trend of fire development.Experimental results show that the proposed method can effectively analyze the fire development situation on the basis of accurate fire segmentation,and provide an effective help for fire detection and control.
作者 宁阳 杜建超 韩硕 杨传凯 NING Yang;DU Jianchao;HAN Shuo;YANG Chuankai(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China;Electric Power Research Institute of State Grid,Shaanxi Electric Power Company,Xi’an 710100,China)
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2021年第5期38-46,共9页 Journal of Xidian University
基金 陕西省重点研发计划(2020GY-058)。
关键词 深度学习 火焰分割 DeeplabV3+ 火情分析 deep learning fire segmentation deeplabV3+ fire analysis
  • 相关文献

参考文献7

二级参考文献35

  • 1易正明,吕子剑,刘志明.氧化铝回转窑火焰图像处理与特征提取[J].仪器仪表学报,2006,27(8):969-972. 被引量:15
  • 2Keogh E, Chakrabarti K, Pazzani M, et al. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases[J]. Journal of Knowledge and Information Systems, 2001, 3(3): 263-286.
  • 3Qu Yunyao, Wang Changzhou. Supporting Fast Search in Time Series for Movement Patterns in Multiples Scales[C]//Proc. of the 7th ACM CIKM Int'l Conference on Information and Knowledge Management. Bethesda, USA: [s. n.], 1998.
  • 4Keogh E, Pazzani M. An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback[C]//Proc. of the 4th Int'l Conference on Knowledge Discovery and Data Mining. New York, USA: [s. n.], 1998.
  • 5Park S, Lee D. Fast Retrieval of Similar Subsequences in Long Sequence Databases[C]//Proc. of the 3rd IEEE Knowledge and Data Engineering Exchange Workshop. Chicago, USA: [s. n.], 1999.
  • 6Perng C S, Wang Haixun. Landmarks: A New Model for Similarity-based Pattern in Time Series Databases[C]//Proc. of the 16th IEEE Int'l Conf. on Data Engineering. California, USA: [s. n.], 2000.
  • 7Pratt K B, Eugene F. Search for Patterns in Compressed Time Series[J]. International Journal of Image and Graphics, 2002, 2(1): 89-106.
  • 8Fu Tak-chung, Chung Fulai, Luk R, et al. Representing Financial Time Series Based on Data Point Importance[Z]. (2007-04-09). http://www.elsevier.com/locate/engappai.
  • 9孙鹏,周晓杰,柴天佑.基于纹理粗糙度的回转窑火焰图像FCM分割方法[J].系统仿真学报,2008,20(16):4438-4441. 被引量:9
  • 10刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4):622-635. 被引量:430

共引文献99

同被引文献63

引证文献9

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部