Forest fires have emerged as a significant global concern, exacerbated by both global
warming and the expanding human population. Several adverse outcomes can result from …

To tackle the problem of missed detections in long-range detection scenarios caused by the
small size of forest fire targets, initiatives have been undertaken to enhance the feature …

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 …

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 …

To overcome low efficiency and accuracy of existing forest fire detection algorithms, this
paper proposes a network model to enhance the real-time and robustness of detection. This …

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 …

For the detection of fire-like targets in indoor, outdoor and forest fire images, as well as fire
detection under different natural lights, an improved YOLOv5 fire detection deep learning …

In the context of large-scale fire areas and complex forest environments, the task of
identifying the subtle features and aspects of fire can pose a significant challenge for the …

Viewed as a significant natural disaster, wildfires present a serious threat to human
communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has …