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

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 …

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 …

Deep learning-based forest fire vision monitoring methods have developed rapidly and are
becoming mainstream. The existing methods, however, are based on enormous amounts of …

Forest resources safety is of paramount importance for natural and public security. Forest fire
detection methods have been attracted much attention recently, but the performance in …

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

Wildfire poses a significant threat and is considered a severe natural disaster, which
endangers forest resources, wildlife, and human livelihoods. In recent times, there has been …

It is well-established that contact fire sensors are susceptible to interference from non-fire
particles and cannot be applied to fire alarms in both large indoor and outdoor open spaces …

Accidental fire outbreak threatens people's life and property safety, and it is of great
significance to study fire detection and alarm early. The detection range of traditional fire …

Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging
task. The traditional image processing method relies heavily on manmade features, which is …