False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning

Yeunghak Lee,Jaechang Shim

doi:10.3390/electronics8101167

Abstract

Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.

Highlights

Many civilian fire injuries and civilian fire deaths occur each year due to intentionally-set fires and naturally occurring fires, which causes much property damage
We proposed a new algorithm using similarity and color histogram of global and local area in the frame to reduceasmoke false positive rate generated bycolor fire detection systems using
We describe a new fire flame and smoke method tomethod remove to false positive detection using spatialusing and temporal features basedfeatures on deepbased learning detection remove false positive detection spatial and temporal on from surveillance cameras

Summary

Introduction

Many civilian fire injuries and civilian fire deaths occur each year due to intentionally-set fires and naturally occurring fires, which causes much property damage. Research on automatic fire detection or monitoring has long been the focus of the interior structure fires and non-residential structure fires to protect casualties and property damage from fires. Sometimes the flames start first; both smoke and flames require early detection to extinguish the fire early. Many methods of detecting smoke and flames to extinguish a fire early have been studied. In order to reduce the damage caused by fire, many early fire detection systems using heat sensors, smoke sensors, and flame detection sensors that detect flames by infrared rays (spectrum) and ultraviolet rays (spectrum) are frequently used [2,3]. Factories, and interior spaces detect the presence of particles produced by fire flames and smoke in close proximity using a chemical reaction by ionization

Methods

Results

Conclusion