Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (5): 147-163.doi: 10.3969/j.issn.1000-5641.2023.05.013

• Data Analytics • Previous Articles    

Smoke detection based on spatial and frequency domain methods

Lianjun SHENG1, Zhixuan TANG2, Xiaoliang MAO1, Fan BAI3, Dingjiang HUANG2,*()   

  1. 1. Fengxian Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai 201499, China
    2. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
    3. Shanghai Thinking Things Network Technology Co. Ltd., Shanghai 200439, China
  • Received:2023-07-01 Online:2023-09-25 Published:2023-09-15
  • Contact: Dingjiang HUANG


In industrial scenarios such as substations, video-based visual smoke detection has been adopted as a new environmental monitoring method to assist or replace smoke sensors. However, in industrial applications, visual smoke detection algorithms are required to maintain a low false detection rate while minimizing the missed detection rate. To address this, this study proposes a smoke detection algorithm based on spatial and frequency domain methods, which perform smoke detection in both domains. In the spatial domain, in addition to extracting smoke motion characteristics, this study designed a method for extracting smoke mask characteristics, which effectively ensures a low missed detection rate. In the frequency domain, this study combined filtering and neural network modules to further reduce the false detection rate. Finally, a fusion domain post-processing strategy was designed to obtain the final detection results. In experiments conducted on a test dataset, the smoke detection algorithm achieved a false detection rate of 0.053 and missed detection rate of 0.113, demonstrating a good balance between false alarms and missed detections, which is suitable for smoke detection in substation industrial scenes.

Key words: visual smoke detection, spatial and frequency domains, neural network, three-dimensional Fourier transform

CLC Number: