A lightweight global awareness deep network model for flame and smoke detection

Aiming at the trouble of low detection accuracy and the problem of large model size,this paper proposes a lightweight flame-and-smoke detection model depending on global awareness of images.The proposed method replaces the Conv+BatchNorm+SiLU(CBS)module of original you only look once version 5(YOLOv5)in the backbone with DSConv+BatchNorm+SiLU(DBS),and the C3 module with GC3,and thus constructs a lightweight backbone network.Besides,involution(InvC3)module is proposed to enhance the global modeling ability and compress the model size,and a module using adaptive receptive fields,named FConv,is proposed to enhance the model’s perception capacity for foreground complex flame-and-smoke information in feature maps.Experimental results show that the proposed model increases the mean average precision of all categories at 0.5 IOU(mAP@0.5)to 70.8%,the mAP@0.5:0.95 to 39.7%,reduces the number of parameters to 3.57M and the amount of calculation to 7.4 giga floating-point operations per second(GFLOPs)under the premise of ensuring the detection speed.It has been verified that the model can achieve high-precision real-time detection of flame and smoke.