Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several C3 modules in the final layer of a backbone network to enhance both the contact of the backbone network to global in-formation and the extraction of flame and smoke features; second, a light bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the model while improving the feature extraction and balancing speed and accuracy features during a fire-detection procedure; third, a global attention mechanism (GAM) is fused into the network to cause the model to focus more on the global dimensional features and further improve the detection accuracy of the model; and finally, the Mish activation function and SIoU loss are utilized to simultaneously increase the convergence speed and enhance the accuracy. The experimental results show that compared to the original algorithm, the mean average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of parameters decreases by 27.1%, and the floating point operations (FLOPs) decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect targets in complex fire scenarios in real time.

翻译：火灾探测技术对于成功的防火措施非常重要。基于图像的火灾探测是一种有效的方法。目前,物体探测算法在复杂的火灾情景中应用时,在执行探测速度和准确性任务方面有缺陷。在本研究中,提出了轻量火灾探测算法,即Light-YOLOv5 (“你只看一遍”第五版)。首先,使用一个可分离的视觉变异器(SepViT)块来取代主干网最后一层的几个C3模块,以加强主干网与全球成形和提取火焰和烟雾特征的接触;第二,一个光双向特征金字塔网络(Light-BIFPN)的设计是为了在火灾探测程序期间,在改进特征提取和平衡速度和准确性特征的同时,将一个全球关注机制(GAM)连接到网络,使模型更加侧重于全球尺寸特征,并进一步提高模型的探测精确度;最后,Mish激活功能和SIO的火焰和火焰特征特征的提取和提取;同时,通过FLF%平均速度的测算法,将原始测算结果的精确度提高3.O的初始测算结果,以提高FLV的精确度,使FLV的原始测算速度和平均测算的精确度提高。