Intense, large-scale forest fires are damaging and very challenging to control. Locations,
where various types of fire behavior occur, vary depending on environmental factors …

Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems
and humans. Deep learning techniques can adaptively learn and extract features of forest …

The frequent occurrence of forest fires causes irreparable damage to the environment and
the economy. Therefore, the accurate detection of forest fires is particularly important. Due to …

Forest fires are highly unpredictable and extremely destructive. Traditional methods of
manual inspection, sensor-based detection, satellite remote sensing and computer vision …

Forest fires occur frequently around the world, causing serious economic losses and human
casualties. Deep learning techniques based on convolutional neural networks (CNN) are …

Forest fire detection is a challenging problem in computer vision. In this paper, we build a
challenging fire dataset which contains images of fire, smoke, and red leaf to better simulate …

Accurate fire identification can help to control fires. Traditional fire detection methods are
mainly based on temperature or smoke detectors. These detectors are susceptible to …

Accurate monitoring of fire and smoke plays an irreplaceable role in preventing fires and
safeguarding the safety of citizens' lives and property. The network structure of YOLOv5 is …

Flame detection is a key technical link to realize intelligent forest fire prevention and control.
However, the current fire detection methods generally have the problems of low detection …

The technologies and models based on machine vision are widely used for early wildfire
detection. Due to the broadness of wild scene and the occlusion of the vegetation, smoke is …