CNN-Based Lightweight Flame Detection Method in Complex Scenes

被引：0
|
作者：
Li X. [1 ,2 ]
Zhang D. [3 ]
Sun L. [4 ]
Xu Y. [1 ,2 ]
机构：
[1] School of Computer Science and Technology, Harbin Institute of Technology(Shenzhen), Shenzhen
[2] Shenzhen Key Laboratory of Visual Object Detection and Re-cognition, Harbin Institute of Technology(Shenzhen), Shenzhen
[3] Liangjiang Artificial Intelligence Academy, Chongqing University of Technology, Chongqing
[4] College of Computer Science and Technology, Guizhou University, Guiyang
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 05期
关键词：
Data augmentation; Depthwise separable convolution; Fire detection; Object detection; You only look once(YOLO) algorithm;
D O I：
10.16451/j.cnki.issn1003-6059.202105004
中图分类号：
学科分类号：
摘要：
The existing fire detection methods rely on high-performance machines, and therefore the speeds on the embedded terminals and the mobile ones are not satisfactory. For most of the detection methods, the speed is low and the false detection rate is high, especially for small-scale fires missed detection problems. To solve these problems, a fire detection method based on you only look once is proposed. Depthwise separable convolution is employed to improve its network structure. Multiple data augmentation and bounding box based loss function are utilized to achieve a higher accuracy. The real-time 21ms fire detection on embedded mobile system is realized through parameter tuning with the detection accuracy ensured. Experimental results show that the proposed method improves accuracy and speed on the fire dataset. © 2021, Science Press. All right reserved.
引用
收藏
页码：415 / 422
页数：7
相关论文
共 21 条
  • [1] ZHOU X L, YU F X, WEN Y C, Et al., Early Fire Detection Based on Flame Contours in Video, Information Technology Journal, 9, 5, pp. 899-908, (2010)
  • [2] SEEBAMRUNGSAT J, PRAISING S, RIYAMONGKOL P., Fire Detection in the Buildings Using Image Processing, Proc of the 3rd ICT International Student Project Conference, pp. 95-98, (2014)
  • [3] CHEN T H, WU P H, CHIOU Y C., An Early Fire-Detection Method Based on Image Processing, Proc of the International Conference on Image Processing, III, pp. 1707-1710, (2004)
  • [4] WANG T, SHI L, YUAN P, Et al., A New Fire Detection Method Based on Flame Color Dispersion and Similarity in Consecutive Frames, Proc of the Chinese Automation Congress, pp. 151-156, (2017)
  • [5] CHEN L, HUANG J F., Flame Detection Method Based on Video, Computer Engineering and Design, 35, 9, pp. 3143-3147, (2014)
  • [6] WANG H, SHAN W J, FANG B F., Multi-layers Context Convolutional Neural Network for Object Detection, Pattern Recognition and Artificial Intelligence, 33, 2, pp. 113-120, (2020)
  • [7] MUHAMMAD K, AHMAD J, MEHMOOD I, Et al., Convolutional Neural Networks Based Fire Detection in Surveillance Videos, IEEE Access, 6, pp. 18174-18183, (2018)
  • [8] DENG J, YAO H W, WANG W F, Et al., Detection Method for Video Flame Based on Optimized InceptionV1
  • [9] ZHAO F Y, LUO B, LIN G J, Et al., Flame Detection Based on Improved YOLOv3, China Sciencepaper, 15, 7, pp. 820-826, (2020)
  • [10] REDMON J, DIVVALA S, GIRSHICK R, Et al., You Only Look Once: Unified, Real-Time Object Detection, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, (2016)
123