Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images

Abstract

Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection.

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

“…It is worth mentioning that most of the institutions and agencies aiming to support wildfire management at the national and regional level use either satellites or combine them with a small fleet of planes to detect and map the extent, spread, and impact of forest fires [ 130 , 131 ]. Furthermore, various organizations have installed network-connected optical cameras in or near wildland areas sharing live images on the web to assist early forest fire detection [ 132 ].…”

“…Also, the terrestrial and aerial-based systems can detect fires at a very early stage depending on their distance from the fire and their spatial resolution in parallel with short latency time [ 132 ]. In contrast, regarding satellite-based systems, time latency and minimum detectable fire size are expected to be improved in the following years.…”

On the Use of Cameras for the Detection of Critical Events in Sensors-Based Emergency Alerting Systems

“…In fact, the last decades have seen a tremendous evolution of visual computing algorithms, allowing fast and more confident pattern detection through the processing of images. This trend has even benefited from the development of artificial intelligence algorithms based on the deep learning paradigm, opening new possibilities in this area [37,38]. Actually, when considering the proposed emergency alerting system, we are concerned with the structured detection of new events and the generation of emergency alarms, but how new events will be detected is out of the scope of the proposed systems, assuring high flexibility to it.…”

“…Another promising issue for visual sensing is machine learning [37], which can also be exploited along with data mining from social media and supportive IoT systems. When data from different sources are considered, the probability of issuing correct and fast emergency alarms is increased, which is the objective of emergencies management in urban areas.…”

An Early Flame Detection System Based on Image Block Threshold Selection Using Knowledge of Local and Global Feature Analysis

“…The system has a 3.5% false alarm rate, 97% detection rate, and processing time of 5 ms per frame. Govil et al [32] proposed a smoke detection system based on machine-learning-based image recognition software and a cloud-based workflow that is capable of scanning hundreds of cameras every minute. The authors further plan to leverage the other existing fire detection algorithms and combine their information with the information obtained from the author's camera-based detection system to provide a robust and faster and combined system.…”