CNN-Based Lightweight Flame Detection Method in Complex Scenes
LI Xinjian1,2, ZHANG Dasheng3, SUN Lilei4, XU Yong1,2
1. School of Computer Science and Technology, Harbin Institute of Technology(Shenzhen), Shenzhen 518055; 2. Shenzhen Key Laboratory of Visual Object Detection and Re-cognition, Harbin Institute of Technology(Shenzhen), Shen-zhen 518055; 3. Liangjiang Artificial Intelligence Academy, Chongqing University of Technology, Chongqing 401135; 4. College of Computer Science and Technology, Guizhou University, Guiyang 550025
Abstract The existing fire detection methods rely on high-performance machines, and therefore the speeds on the embedded terminals and the mobile ones are not satisfactory. For most of the detection methods, the speed is low and the false detection rate is high, especially for small-scale fires missed detection problems. To solve these problems, a fire detection method based on you only look once is proposed. Depthwise separable convolution is employed to improve its network structure. Multiple data augmentation and bounding box based loss function are utilized to achieve a higher accuracy. The real-time 21ms fire detection on embedded mobile system is realized through parameter tuning with the detection accuracy ensured. Experimental results show that the proposed method improves accuracy and speed on the fire dataset.
Fund:Shenzhen Science and Technology Project(No. ZDSYS20190902093015527)
Corresponding Authors:
XU Yong, Ph.D., professor. His research interests include pa-ttern recognition, image processing, deep learning, biometric recognition and bioinformatics.
About author:: LI Xinjian, master student. His research interests include computer vision and object detection.ZHANG Dasheng, master student. His research interests include computer vision and object detection.SUN Lilei, Ph.D. candidate. His research interests include pattern recognition and biometric recognition.
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2021, Vol. 34 
