A visual real-time fire detection using single shot multibox detector for uav-based fire surveillance
2020 IEEE Eighth International Conference on Communications and …, 2021•ieeexplore.ieee.org
Early fire detection and alarm are significantly important to reduce the losses caused by fire.
Conventional methods in fire detection using smoke and heat detectors have disadvantages
in accuracy, latency as well as the detection area. In this paper, we propose and implement
a real-time fire detection solution for large area surveillance using the unmanned aerial
vehicle with an integrated visual detection and alarm system. The system includes a low-
cost camera, a light weight companion computer, a flight controller as well as localization …
Conventional methods in fire detection using smoke and heat detectors have disadvantages
in accuracy, latency as well as the detection area. In this paper, we propose and implement
a real-time fire detection solution for large area surveillance using the unmanned aerial
vehicle with an integrated visual detection and alarm system. The system includes a low-
cost camera, a light weight companion computer, a flight controller as well as localization …
Early fire detection and alarm are significantly important to reduce the losses caused by fire. Conventional methods in fire detection using smoke and heat detectors have disadvantages in accuracy, latency as well as the detection area. In this paper, we propose and implement a real-time fire detection solution for large area surveillance using the unmanned aerial vehicle with an integrated visual detection and alarm system. The system includes a low-cost camera, a light weight companion computer, a flight controller as well as localization and telemetry modules. To achieve real-time detection, Single Shot MultiBox Detector (SSD) algorithm is implemented as the heart of the system. We used MobileNets base model, which more efficient for mobile and embedded vision applications, instead of conventional VGG-16/ResNet model to achieve the mean average precision of 92.7% with the detection speed of 26 FPS.
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