  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 algorithm is proposed. The YOLOv5 detection
model expands the feature extraction network from three dimensions, which
enhances feature propagation of fire small targets identification, improves
network performance, and reduces model parameters. Furthermore, through the
promotion of the feature pyramid, the top-performing prediction box is
obtained. Fire-YOLOv5 attains excellent results compared to state-of-the-art
object detection networks, notably in the detection of small targets of fire
and smoke with mAP 90.5% and f1 score 88%. Overall, the Fire-YOLOv5 detection
model can effectively deal with the inspection of small fire targets, as well
as fire-like and smoke-like objects with F1 score 0.88. When the input image
size is 416 x 416 resolution, the average detection time is 0.12 s per frame,
which can provide real-time forest fire detection. Moreover, the algorithm
proposed in this paper can also be applied to small target detection under
other complicated situations. The proposed system shows an improved approach in
all fire detection metrics such as precision, recall, and mean average
precision.
