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.

翻译：针对室内、室外及森林火灾图像中类火焰目标的检测，以及不同自然光照条件下的火灾检测问题，提出一种改进的YOLOv5深度学习火灾检测算法。该YOLOv5检测模型从三个维度扩展特征提取网络，增强了火焰小目标识别的特征传播能力，提升了网络性能并减少了模型参数。此外，通过优化特征金字塔结构，获得了性能最优的预测框。与当前最先进的目标检测网络相比，Fire-YOLOv5在火焰与烟雾小目标检测方面表现优异，平均精度均值（mAP）达90.5%，F1分数为88%。总体而言，Fire-YOLOv5检测模型可有效处理火焰小目标及类火焰、类烟雾物体的检测任务，F1分数达0.88。当输入图像分辨率为416×416时，单帧平均检测时间为0.12秒，能够实现森林火灾的实时检测。此外，本文提出的算法还可应用于其他复杂场景下的小目标检测。该系统在精确率、召回率和平均精度均值等所有火灾检测指标上均展现出改进效果。