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 …

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 …

Aiming at the problem that the existing target detection algorithms are difficult to detect
sudden fire with high precision in real-time in the complex forest environment, an improved …

Traditional forest fire detection based on sensing devices has a small range, low sensitivity
and high false alarm rate, while existing forest fire detection methods based on deep …

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires
can help minimize the damage to ecosystems and forest life. In this paper, we propose an …

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 …

A new algorithm for light-weight forest fire object detection was proposed based on
YOLOv5s to address the low accuracy, poor flexibility, and high software and hardware …

Early fire detection is quite important to fighting of forest fire. A lightweight YOLO forest fire
detection model is proposed in this study. First, the YOLOv4 object detection model is …

Forest fire is a severe natural disaster, which leads to the destruction of forest ecology. At
present, fire detection technology represented by convolutional neural network is widely …

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 …