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 …

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

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 …

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 …

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 …

Forest fires present a significant challenge to ecosystems, particularly due to factors like tree
cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are …

Massive wildfires have become more frequent, seriously threatening the Earth's ecosystems
and human societies. Recognizing smoke from forest fires is critical to extinguishing them at …

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 …

Sensor-based methods and vision-based methods with manual features selection have
been widely used in forest fire detection. However, they have limited detection areas and are …