XIAO Bowei,YAN Chunman.A lightweight global awareness deep network model for flame and smoke detection[J].Optoelectronics Letters,2023,(10):614-622 A lightweight global awareness deep network model for flame and smoke detection Author Name Affiliation XIAO Bowei  School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China  YAN Chunman  School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China  Abstract:       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. Hits: 172 Download times: 0 View Full Text    Download reader