Development of early fire detection model for buildings using computer vision-based CCTV

A fire in a building directly affects the lives of its occupants. Therefore, a safer environmental system that can minimize the damage caused by a fire occurring indoors needs to be developed. Recent studies on computer vision-based rapid fire detection methods aim to overcome the limitations of general fire detectors and prevent false alarms using advanced deep learning technology. However, studies considering the development of a video fire detection model for indoor usage and its implementation in an actual test room are lacking. We developed a computer vision-based early fire detection model (EFDM) using an indoor closed-circuit television (CCTV) surveillance. The proposed EFDM derives the fire detection time through actual fire tests. The possibility and necessity of using fire detectors indoors is confirmed by comparing the results with the fire detection times of a general fire detector. The developed model achieves a recall, precision, and mAP_0.5 performance of 0.97, 0.91, and 0.96, respectively. The fire recorded in the indoor fire video test dataset was detected within 8 s. The possibility of fire detection from three combustibles according to Underwriters Laboratories (UL) 268 B is confirmed via experimentation. A difference of up to 307 s is observed when the fire detection times of the EFDM and general fire detectors are compared. The useable range is confirmed by detecting a fire within 1 s of the maximum visible range of the CCTV. The proposed method can help contribute to the reduction of unfortunate property damages or casualties caused by a fire.