Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (6): 301-304.doi: 10.11896/j.issn.1002-137X.2019.06.045

Previous Articles     Next Articles

Video Fire Detection Method Based on YOLOv2

DU Chen-xi1,2, YAN Yun-yang1,2, LIU Yi-an1, GAO Shang-bing2   

  1. (School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)1
    (Faculty of Computer & Software Engineering,Huaiyin Institute of Technology,Huaian,Jiangsu 223003,China)2
  • Received:2018-06-01 Published:2019-06-24

PDF (PC)

1204

Abstract: It is difficult for general flame detection methods to adapt to complex scenes,so the detection rates is low.This paper proposed a deep learning flame detection method based on an improved YOLOv2 network to extract the flame features automatically.In order to avoid the information loss in the feature extraction process,the selected anchor box by clustering is suggested and multi-scale feature fusion method is used to fuse high-level and shallow feature information,to further improve the detection rate of the model.Experimental results on the Bilkent University flame video dataset show that the average true inspection rate of the proposed method is 98.8%,and the detection rate is 40 frames/s,so its robustness and real-time performance are strong.

Key words: Clustering, Feature fusion, Fire detection, Multi-scale feature, YOLOv2

CLC Number: 

  • TP391
[1]DIMITROPOULOS K,BARMPOUTIS P,GRAMMALIDIS N.Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection[J].IEEE Tran-sactions on Circuits & Systems for Video Technology,2015,25(2):339-351.
[2]CELIK T,DEMIREL H,OZKARAMANLI H.Automatic fire detection in video sequences[C]∥European Signal Processing Conference.IEEE,2015:1-5.
[3]POOBALAN K,LIEW S C.Fire detection algorithm using ima-ge processing techniques[C]∥International Conference on Artificial Intelligence and Computer Science.2015.
[4]GENG Q T,YU F H,ZHAO H W,et al.New algorithm of flame detection based on color features[J].Journal of Jilin University(Engineering and Technology Edition),2014,44(6):1787-1792.(in Chinese)
耿庆田,于繁华,赵宏伟,等.基于颜色特征的火焰检测新算法[J].吉林大学学报(工学版),2014,44(6):1787-1792.
[5]FRIZZI S,KAABI R,BOUCHOUICHA M,et al.Convolutional neural network for video fire and smoke detection[C]∥Confe-rence of the IEEE Industrial Electronics Society.IEEE,2016:877-882.
[6]MUHAMMAD K,AHMAD J,MEHMOOD I,et al.Convolu-tionalNeural Networks based Fire Detection in Surveillance Vi-deos[J].IEEE Access,2018,PP(99):1.
[7]ZHANG H,WANG K F,WANG F Y.Advances and Perspectives on Applications of Deep Learning in Visual Object Detection[J].Acta Automatic Sinica,2017,43(8):1289-1305.(in Chinese)
张慧,王坤峰,王飞跃.深度学习在目标视觉检测中的应用进展与展望[J].自动化学报,2017,43(8):1289-1305.
[8]REDMON J,FARHADI A.YOLO9000:Better,faster,stronger[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Washington:IEEE Computer Society,2017:6517-6525.
[9]REN S,GIRSHICK R,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[10] HE K,ZHANG X,REN S,et al.Identity Mappings in Deep Residual Networks[C]∥Computer Vision－ECCV 2016.Springer International Publishing,2016:630-645.
[11] PINHEIRO P O,LIN T Y,COLLOBERT R,et al.Learning to refine object segments[C]∥Lecture Notes in Computer Scien-ce.Heidelberg.Springer,2016:75-91.
[12] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot MultiBox Detector[C]∥European Conference on Computer Vision.Springer,Cham,2016:21-37.
[13]HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv preprint arXiv:1704.04861,2017.
[1] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[2] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[3] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[4] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[5] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
[6] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[7] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[8] CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[9] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[10] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[11] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[12] LAN Ling-xiang, CHI Ming-min. Remote Sensing Change Detection Based on Feature Fusion and Attention Network [J]. Computer Science, 2022, 49(6): 193-198.
[13] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[14] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[15] FAN Xin-nan, ZHAO Zhong-xin, YAN Wei, YAN Xi-jun, SHI Peng-fei. Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism [J]. Computer Science, 2022, 49(5): 50-57.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.