一种改进DeeplabV3网络的烟雾分割算法

doi:10.19665/j.issn1001-2400.2019.06.008

Abstract

Abstract:

Existing smoke detection methods depend mostly on features which are selected manually and the smoke areas in video images often cannot be segmented accurately. This paper proposes an improved DeeplabV3 smoke segmentation algorithm based on this. A feature refinement module is added after the basic encoder network to weaken the gridding effects caused by dilated convolutions. For the non-rigid objects such as smoke with variable scales and postures, the Atrous Spatial Pyramid Pooling module is combined with the deformable convolution to better adapt to the smoke deformation. And a channel attention decoder module is proposed to further restore the spatial details of smoke images. According to the test of the smoke image data set, the proposed model has a faster average prediction speed of 71.73ms per image. Besides, compared with the DeeplabV3 network, this algorithm can lead to a higher MPA (Mean Pixel Accuracy) of 97.78% and a higher MIoU (Mean Intersection over Union) of 91.21%, thus improving the performance by 0.56% and 2.17% respectively, and making it more suitable for smoke segmentation. Public smoke video test results show that this model outperforms other video-based smoke detection methods for the detection rate, and that it is of certain research significance and practical value.

Key words: image processing, smoke detection, semantic segmentation, deformable convolution, attention mechanism, deep learning

CLC Number:

TP391

WANG Ziyi,SU Yuting,LIU Yanyan,ZHANG Wei. Algorithm for segmentation of smoke using the improved DeeplabV3 network[J].Journal of Xidian University, 2019, 46(6): 52-59.

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References 15

[1]	MUHAMMAD K, AHMAD J, MEHMOOD I , et al. Convolutional Neural Networks Based Fire Detection in Surveillance Videos[J]. IEEE Access, 2018,6:18174-18183. doi: 10.1109/ACCESS.2018.2812835
[2]	ZHANG Q X, LIN G H, ZHANG Y M , et al. Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images[C]//Procedia Engineering: 211. Oxford: Elsevier Ltd, 2018: 441-446.
[3]	YUAN F, ZHANG L, XIA X , et al. Deep Smoke Segmentation[J]. Neurocomputing, 2019,357:248-260. doi: 10.1109/TIP.2019.2946126 pmid: 31613768
[4]	LONG J, SHELHAMER E, DARRELL T . Fully Convolutional Networks for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,39(4):640-651. doi: 10.1109/TPAMI.2016.2572683 pmid: 27244717
[5]	CHEN L C, PAPANDREOU G, KOKKINOS I , et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs[C]//Conference Track Proceedings of the 2015 3rd International Conference on Learning Representations. San Diego: International Conference on Learning Representations, 2015: 149801.
[6]	CHEN L C, PAPANDREOU G, KOKKINOS I , et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFS[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4):834-848. doi: 10.1109/TPAMI.2017.2699184 pmid: 28463186
[7]	CHEN L C, PAPANDREOU G, SCHROFF F , et al. Rethinking Atrous Convolution for Semantic Image Segmentation[J/OL]. [2019-05-20]. https://arxiv.org/abs/1706.05587.
[8]	YU F, KOLTUN V, FUNKHOUSER T . Dilated Residual Networks[C]//Proceedings of the 2017 30th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 636-644.
[9]	DAI J, QI H, XIONG Y , et al. Deformable Convolutional Networks[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 764-773.
[10]	HU J, SHEN L, SUN G . Squeeze-and-excitation Networks[C]//Proceedings of the 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2018: 7132-7141.
[11]	RONNEBERGER O, FISCHER P, BROX T . U-Net: Convolutional Networks for Biomedical Image Segmentation[C]//Lecture Notes in Computer Science: 9351. Heidelberg: Springer Verlag, 2015: 234-241.
[12]	ZHAO H, SHI J, QI X , et al. Pyramid Scene Parsing Network[C]//Proceedings of the 2017 30th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 2881-2890.
[13]	赵敏, 张为, 王鑫 , 等. 时空背景模型下结合多种纹理特征的烟雾检测[J]. 西安交通大学学报, 2018,52(8):72-78.
	ZHAO Min, ZHANG Wei, WANG Xin , et al. Smoke Detection via Multi-texture Feature Exploration with Spatio-temporal Background Model[J]. Journal of Xi’an Jiaotong University, 2018,52(8):72-78.
[14]	BESBES O, BENAZZA-BENYAHIA A . A Novel Video-based Smoke Detection Method Based on Color Invariants[C]//Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2016: 1911-1915.
[15]	BARMPOUTIS P, DIMITROPOULOS K, GRAMMALIDIS N . Smoke Detection Using Spatio-temporal Analysis, Motion Modeling and Dynamic Texture Recognition[C]//Proceedings of the 2014 European Signal Processing Conference. Poland: European Signal Processing Conference, 2014: 1078-1082.

算法	MPA/%	MIoU/%	时间/ms
UNet^[11]	93.99	83.43	86.38
PSPNet^[12]	95.76	88.60	108.86
DeeplabV3^[7](base)	97.22	89.04	89.86
Ours1	97.49	89.99	67.05
Ours2	97.70	90.92	67.12
Ours3	97.78	91.21	71.73

烟雾视频	Ours3		文献[13]		文献[14]		文献[15]
烟雾视频	R_TPR/ %	R_TNR/ %	R_TPR/ %	R_TNR/ %	R_TPR/ %	R_TNR/ %	R_TPR/ %
sBehindtheFence	98.26	94.60	97.20	96.27	94.72	100	94.44
sBtFence2	98.20	100	98.17	100	99.08	100	98.71
sMoky	98.85	100	99.68	100	86.23	100	99.78
sWasteBasket	99.41	100	97.18	98.36	99.89	92.60	99.29
sWindow	98.40	100	98.10	100	94.30	100	88.52
平均值	98.56	98.92	98.06	98.83	94.84	98.52	96.15

Algorithm for segmentation of smoke using the improved DeeplabV3 network

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