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”

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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
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