一种双网融合的分阶段烟雾检测算法

doi:10.19665/j.issn1001-2400.2020.04.019

Abstract

Abstract:

Existing video smoke detection methods have a low detection accuracy in complex scenes and cannot detect smoke areas in video frames accurately. In this paper, a phased smoke detection algorithm that combines the smoke movement process and the target detection algorithm is proposed. First, an improved ViBe algorithm based on smoke color features is used to extract the continuously moving smoke in video. Then, the YOLO v3 model is used as the target detection network. The channel attention mechanism is added to the residual structure of its backbone network. Focal-loss and GIoU are utilized to improve the loss function. According to the test of the smoke image data set, the detection time of the improved network on a single picture is 38.4ms and the mAP reaches 92.13%, which is 2.19% higher than that by the original model. While extracting smoke motion, the same frame is sent to the improved YOLO v3 for smoke detection. Finally, comprehensive discrimination is made based on the smoke detection results in stages. Public smoke video test results show that the algorithm has an average detection rate of 98.88%, which proves that the algorithm has a strong adaptability, a high detection efficiency in complex scenes and a high practical application value.

Key words: smoke detection, ViBe algorithm, YOLO v3, attention mechanism, deep learning

CLC Number:

TP391

DU Lizhao,XU Yan,ZHANG Wei. Phased smoke detection algorithm using dual network fusion[J].Journal of Xidian University, 2020, 47(4): 141-148.

Figures/Tables 9

References 15

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模型	mAP/%	时间/ms
SSD	87.56	52.6
YOLOV3	89.94	37.1
Ours1	91.29	38.4
Ours2	92.13	38.4

烟雾视频	文中算法		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/ %	R_TNR/ %
sBehindtheFence	98.86	97.60	97.20	96.27	94.72	100	98.26	94.60
sBtFence2	98.40	100	98.17	100	99.08	100	98.20	100
sMoky	98.91	100	99.68	100	86.23	100	98.85	100
sWasteBasket	99.45	99.12	97.18	98.36	99.89	92.60	99.41	100
sWindow	98.80	100	98.10	100	94.30	100	98.40	100
均值	98.88	99.34	98.06	98.83	94.84	98.52	98.56	98.92

Phased smoke detection algorithm using dual network fusion

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