Abstract
With the increasing interest in leveraging mobile robotics for fire detection and monitoring arises the need to design recognition technology systems for these extreme environments. This work focuses on evaluating the sensing capabilities and image processing pipeline of thermal imaging sensors for fire detection applications, paving the way for the development of autonomous systems for early warning and monitoring of fire events. The contributions of this work are threefold. First, we overview image processing algorithms used in thermal imaging regarding data compression and image enhancement. Second, we present a method for data-driven thermal imaging analysis designed for fire situation awareness in robotic perception. A study is undertaken to test the behavior of the thermal cameras in controlled fire scenarios, followed by an in-depth analysis of the experimental data, which reveals the inner workings of these sensors. Third, we discuss key takeaways for the integration of thermal cameras in robotic perception pipelines for autonomous unmanned aerial vehicle (UAV)-based fire surveillance.
Highlights
With the emergent effects of climate change, several regions worldwide have been undergoing an increasing number of more intense and devastating wildfire events, as well as extended fire seasons [1,2]
From the analyses presented throughout this study, it becomes evident that thermal cameras can potentially bring improved reliability for fire detection and monitoring systems in several aspects in which visible range systems have limitations
Adapting thermal imaging cameras for wildfire detection and monitoring systems entails an adequate understanding of the application requirements and capabilities of each device, which change depending on manufacturers and camera models
Summary
With the emergent effects of climate change, several regions worldwide have been undergoing an increasing number of more intense and devastating wildfire events, as well as extended fire seasons [1,2] In this context, given the high spatial and temporal uncertainty intrinsic to these phenomena, environment monitoring is determinant for firefighting activities to mitigate the consequences of these events. By being installed near the ground—at low altitudes—these systems have several limitations. Since these are widely based on visible range sensors, clouds can be confused with smoke and the sunset or reflections can be mistaken by flames, leading to false alarms. Solutions based on satellite data have considerable latency, hindering their application for early detection
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