Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 100-107.doi: 10.6040/j.issn.1672-3961.0.2019.424

• Machine Learning & Data Mining • Previous Articles     Next Articles

Fire detection based on lightweight convolutional neural network

Yunyang YAN1,2,3(),Chenxi DU1,2,Yian LIU2,Shangbing GAO1   

  1. 1. Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huaian 223003, Jiangsu, China
    2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
    3. School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China
  • Received:2019-07-25 Online:2020-04-20 Published:2020-04-16
  • Supported by:
    国家自然科学基金资助项目(61402192);江苏省“六大人才高峰”项目(2013DZXX-023);江苏省“青蓝工程”;淮安市“533英才工程”

RichHTML

13

PDF (PC)

697

Abstract:

A novel lightweight flame detection method was proposed based on MobileNet. The video flame detection rate was promoted by the feature receptive field of DCB(dilated convolution block)module expand based on depthwise separable convolution and dilated convolution to strengthen the feature semantic information. The SSD(single shot multibox detector) detection framework was also optimized. The lightweight detection model DMSSD(Dilated MobileNet-SSD) was provided. Experiments showed that the mean average precision was increased by 1.7% and 3.8% respectively on the PASCAL VOC dataset and the VisiFire dataset of Bilkent University. Furthermore, the detection speed was up to 80 frames per second. The robustness and real-time performance of DMSSD were strong.

Key words: fire detection, MobileNet, dilated convolution, channel shuffle, DCB

CLC Number: 

  • TP391

Fig.1

Structure of MobileNet-SSD model"

Fig.2

Comparison of standard convolution anddilated convolution"

Table 1

Receptive field of convolution layers"

层级 输入尺寸 卷积核尺寸 步长 输出尺寸 感受野
Conv5 38×38 1 1 38×38 43
Conv11 19×19 1 1 19×19 219
Conv13 10×10 1 1 10×10 315
Conv14-2 10×10 3 2 5×5 379
Conv15-2 5×5 3 2 3×3 507
Conv16-2 3×3 3 2 2×2 763
Conv17-2 2×2 3 2 1×1 1 275

Fig.3

Illustration of the gridding problem"

Fig.4

Channel shuffle"

Fig.5

Skip connections"

Fig.6

Dilated convolution block"

Fig.7

Structure of DMSSD model"

Table 2

Effectiveness of various designs onthe VOC 2007 test"

方法 移除Conv16 复合膨胀卷积 通道重排 残差连接 mAP/%
MobileNet 72.7
对照组1 √ 73.0
对照组2 √ √ 74.0
对照组3 √ √ √ 74.2
DMSSD √ √ √ √ 74.4

Table 3

Results on PASCAL VOC 2007"

方法 MACs/
109		 Parameters/
106		 GPU Inference/
ms		 mAP/
%
Tiny-YOLO[16] 3.49 15.86 6.85 57.1
DSOD_smallest[17] 5.29 5.90 18.74 73.6
Pelee[18] 1.21 5.43 14.17 76.4
MobileNet-SSD[1] 1.16 5.77 5.92 72.7
DMSSD 1.25 5.76 6.50 74.4

Table 4

Single class test results on PASCAL VOC 2007"

方法 飞机 自行车 鸟 船 瓶子 大巴 轿车 猫 椅子 奶牛 桌子 狗 马 摩托 人 植物 绵羊 沙发 火车 电视
MobileNet-SSD[1] 73.9 82.4 71.1 61.2 39.1 82.6 80.2 88.2 53.8 67.8 78.4 80.8 87.9 85.6 76.5 43.4 65.0 79.4 86.7 69.6
DMSSD 74.7 83.8 71.3 59.7 40.8 84.6 81.0 88.6 56.8 72.6 77.9 83.2 89.2 86.5 77.3 45.5 71.8 79.7 87.8 75.6

Table 5

Number of samples in the VisiFire dataset"

类别 训练集 测试集 合计
图片 5 224 2 582 7 806
目标 10 920 4 130 15 050

Table 6

Detection results on VisiFire"

模型 MACs/
109		 Parameters/
106		 FPS/
(帧·s-1)		 mAP/
%
MobileNet-SSD[1] 1.13 5.52 84 74.3
DMSSD 1.22 5.49 80 78.1

Fig.8

Comparison of model performance"

Fig.9

Video examples and detection results"

Table 7

Detection results on flame videos"

视频 总帧数 MobileNet-SSD[1] Tiny-YOLO[16] Pelee[18] Tiny-DSOD[19] DMSSD
TP/% FP/% TP/% FP/% TP/% FP/% TP/% FP/% TP/% FP/%
Video1 200 96.0 4.0 94.5 5.5 98.5 1.5 98.0 2.0 97.5 2.5
Video2 216 95.8 4.2 96.8 3.2 98.1 1.9 97.7 2.3 97.2 2.8
Video3 439 90.7 9.3 84.1 15.9 93.2 6.8 91.8 8.2 92.5 7.5
Video4 170 94.7 5.3 97.1 2.9 97.6 2.4 95.3 4.7 96.5 3.5
Video5 595 92.9 7.1 84.5 15.5 96.1 3.9 92.4 7.6 95.0 5.0
Video6 470 92.1 7.9 72.3 27.7 94.7 5.3 91.5 8.8 91.9 8.1
平均 348.3 93.7 6.3 88.2 11.8 96.4 3.6 94.5 5.5 95.1 4.9

Table 8

Detection results on non-flame videos"

视频 总帧数 MobileNet-SSD[1] Tiny-YOLO[16] Pelee[18] Tiny-DSOD[19] DMSSD
TP/% FP/% TP/% FP/% TP/% FP/% TP/% FP/% TP/% FP/%
Video7 144 93.1 6.9 85.4 14.6 95.1 4.9 92.4 7.6 93.8 6.2
Video8 120 93.3 6.7 91.6 8.4 95.0 5.0 94.2 5.8 95.0 5.0
Video9 228 96.9 3.1 94.7 5.3 97.8 2.2 96.1 3.9 97.4 2.6
平均 164 94.4 5.6 90.6 9.4 96.0 4.0 94.2 5.8 95.4 4.6
1 HOWARD A G, ZHU Menglong, CHEN Bo, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv: 1704.04861, 2017. https://arxiv.org/abs/1704.04861.
2 CHENG Yu, WANG Duo, ZHOU Pan, et al. A survey of model compression and acceleration for deep neural networks[J]. arXiv preprint arXiv: 1710.09282, 2017. https://arxiv.org/abs/1710.09282.
3 HAN Song, MAO Huizi, DALLY W J. Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding[J]. arXiv preprint arXiv: 1510.00149, 2015. https://arxiv.org/abs/1510.00149.
4 LUO Ping, ZHU Zhenyao, LIU Ziwei, et al. Face model compression by distilling knowledge from neurons[C]//Proceedings of 30th AAAI Conference on Artificial Intelligence. Menlo Park, USA: Association for the Advancement of Artificial Intelligence, 2016: 3560-3566.
5 JADERBERG M, VEDALDI A, ZISSERMAN A. Speeding up convolutional neural networks with low rank expansions[J]. arXiv preprint arXiv: 1405.3866, 2014. https://arxiv.org/abs/1405.3866.
6 SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE Computer Society, 2016: 2818-2826.
7 CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE Computer Society, 2017: 1800-1807.
8 ZHANG Xiangyu, ZHOU Xinyu, LIN Mengxiao, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE Computer Society, 2018: 6848-6856.
9 CHEN Liangchieh, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv: 1706.05587, 2017. https://arxiv.org/abs/1706.05587.
10 DUMOULIN V, VISIN F. A guide to convolution arithmetic for deep learning[J]. arXiv preprint arXiv: 1603.07285, 2016. https://arxiv.org/abs/1603.07285.
11 WEI S E, RAMAKRISHNA V, KANADE T, et al. Convolutional pose machines[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE Computer Society, 2016: 4724-4732.
12 CAO Xudong. A practical theory for designing very deep convolutional neural network[R/OL].[2018-08-01]. https://www.kaggle.com/c/datasciencebowl/discussion/13166.
13 YU Fisher, KOLTUN V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv: 1511.07122, 2015. https://arxiv.org/abs/1511.07122.
14 WANG Panqu, CHEN Pengfei, YUAN Ye, et al. Understanding convolution for semantic segmentation[C]//2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Nevada, USA: IEEE Computer Society, 2018: 1451-1460.
15 CHEN Liangchieh, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[J]. arXiv preprint arXiv: 1802.02611, 2018. https://arxiv.org/abs/1802.02611
16 REDMON J, FARHADI A. Yolo9000: better, faster, stronger[C]//Computer Vision and Pattern Recognition. Washington, USA: IEEE Computer Society, 2017: 6517-6525.
17 SHEN Zhiqiang, LIU Zhuang, LI Jianguo, et al. Dsod: learning deeply supervised object detectors from scratch[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italia: IEEE Computer Society, 2017: 1919-1927.
18 WANG R J, LI Xiang, LING C X. Pelee: a real-time object detection system on mobile devices[C]//Advances in Neural Information Processing Systems. Montreal, Canada: Neural Information Processing Sys-tems Foundation, Inc., 2018: 1963-1972.
19 LI Yuxi, LI Jiuwei, LIN Weiyao, et al. Tiny-dsod: lightweight object detection for resource-restricted usages[J]. arXiv preprint arXiv: 1807.11013, 2018. https://arxiv.org/abs/1807.11013.
20 ZOPH B, VASUDEVAN V, SHLENS J, et al. Learning transferable architectures for scalable image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE Computer Society, 2018: 8697-8710.
[1] Yue YUAN,Yanli WANG,Kan LIU. Named entity recognition model based on dilated convolutional block architecture [J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 105-114.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] SHI Lai-shun,WAN Zhong-yi . Synthesis and performance evaluation of a novel betaine-type asphalt emulsifier[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 112 -115 .
[2] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[3] SUN Guohua, WU Yaohua, LI Wei. The effect of excise tax control strategy on the supply chain system performance[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 63 -68 .
[4] WANG Yong, XIE Yudong. Gas control technology of largeflow pipe[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 70 -74 .
[5] SUN Cong-zheng,GUAN Cong-sheng,QIN Jing-yu,CHENG Chuan . The structure and performances of the electroless Ni-P alloy coating on aluminum alloy[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(5): 108 -112 .
[6] HU Tian-liang,LI Peng,ZHANG Cheng-rui,ZUO Yi . Design of a QEP decode counter based on VHDL[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(3): 10 -13 .
[7] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 104 -107 .
[8] CHEN Huaxin, CHEN Shuanfa, WANG Binggang. The aging behavior and mechanism of base asphalts[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 125 -130 .
[9] ZHANG Gong-xiao,YANG Rong-hua . Synthesis and characterization of salicylaldehyde methylthiosemicarbazone Schiff base complexes[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(3): 108 -111 .
[10] HANG Guang-qing,KONG Fan-yu,LI Da-xing, . Efficient algorithm with resistance to simple power analysis on Koblitz curves[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(3): 78 -80 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd