Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Real-time Smoke Detection Research with False Positive Reduction using Spatial and Temporal Features based on Faster R-CNN

  • Lee, Sang-Hoon (Research Engineer, Gumi Electronics & Information Technology Research Institute) ;
  • Lee, Yeung-Hak (SW Convergence Education Center, Andong National University)
  • Received : 2020.12.03
  • Accepted : 2020.12.29
  • Published : 2020.12.31
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Abstract

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

Keywords

Acknowledgement

This work supported by the Disaster Safety Research and Development, 20010305, Development of high-stretch material-based chemical protection suits(Level B, C class) with activity and safety through wearer's bio-signal measurement and gas detection, funded by the Ministry of Public Administration and Security.

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