Smoke Recognition Under Transmission Channel Based on Improved YOLOv5s
In response to the challenge of detecting fire hazards in power transmission corridors in a timely manner,especially in the early stages of a fire when irregular and thin smoke is difficult to be detected,which is of great significance for controlling danger-ous situations.To solve this issue,an improved smoke recognition algorithm for YOLOv5s network is proposed.A Convolutional Block Attention Module(CBAM)is introduced into the YOLOv5s model to extract the features of smoke with less distinct outlines.Additionally,the CARAFE feature upsampling algorithm is incorporated to expand the perception field and leverage other image in-formation for capturing deep smoke features.To capture smaller smoke patterns in the images,the Sigmoid Linear Unit(SiLU)is re-placed with the original activation function Funnel ReLU(FReLU),a two-dimensional funnel-shaped activation function is used to ac-tivate insensitive information in the network space while introducing minimal computational overhead and overfitting risks,thereby en-hancing visual task performance.Experimental results demonstrate that the improved algorithm in this project increases the precision by 6.8％,the recall rate by 2.8％,and the mean average precision by 2.3％relative to the original YOLOv5s network.This signifi-cant enhancement in detection accuracy makes it more suitable for practical smoke detection applications.
万方数据
维普
