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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (2): 100-107.doi: 10.6040/j.issn.1672-3961.0.2019.424

• 机器学习与数据挖掘 • 上一篇    下一篇

基于轻型卷积神经网络的火焰检测方法

严云洋1,2,3(),杜晨锡1,2,刘以安2,高尚兵1   

  1. 1. 淮阴工学院计算机与软件工程学院, 江苏 淮安 223003
    2. 江南大学物联网工程学院, 江苏 无锡 214122
    3. 江苏海洋大学计算机工程学院, 江苏 连云港 222005
  • 收稿日期:2019-07-25 出版日期:2020-04-20 发布日期:2020-04-16
  • 作者简介:严云洋(1967—)，男，江苏淮安人，教授，博士，CCF会员，主要研究方向为数字图像处理,模式识别. E-mail：yunyang@hyit.edu.cn
  • 基金资助:
    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”

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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摘要：

提出一种基于MobileNet的轻型火焰检测方法,基于深度分离卷积和膨胀卷积的膨胀卷积模块(dilated convolution block, DCB)扩增特征的感受野,加强特征语义信息,提高了视频火焰目标的检测率;优化SSD(Single Shot Multibox Detector)检测框架,提出了一种轻型的检测模型DMSSD(Dilated MobileNet-SSD)。在PASCAL VOC数据集和Bilkent大学VisiFire数据集上进行火焰检测试验,试验结果表明火焰检测的平均精度均值分别提升了1.7%和3.8%,火焰检测速度也可达80帧/s,具有较强的鲁棒性和实用性。

关键词: 火焰检测, MobileNet, 膨胀卷积, 通道重排, DCB

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

中图分类号: 

  • TP391

图1

MobileNet-SSD模型结构"

图2

常规卷积与膨胀卷积的对比"

表1

各卷积层感受野"

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

图3

网格化示例"

图4

通道重排"

图5

残差连接"

图6

膨胀卷积模块"

图7

DMSSD模型结构"

表2

VOC2007测试集中各组件性能"

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

表3

PASCAL VOC 2007数据集检测结果"

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

表4

PASCAL VOC 2007分类别检测结果"

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

表5

VisiFire数据集中样本数量"

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

表6

VisiFire数据集检测结果"

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

图8

模型性能对比"

图9

视频示例及检测结果"

表7

火焰视频的检测结果"

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

表8

非火焰检测视频结果"

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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[1] 严云洋,张慧珍,刘以安,高尚兵. 基于GMM与三维LBP纹理的视频火焰检测[J]. 山东大学学报 (工学版), 2019, 49(1): 1-9.
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