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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 detect, an improved smoke recognition algorithm for YOLOv5s network is proposed. This method enhances the capability to extract features of smoke with less distinct outlines by introducing a Convolutional Block Attention Module (CBAM) into the YOLOv5s model. Additionally, it incorporates the CARAFE feature upsampling algorithm to expand the perception field and leverage other image information for capturing deep smoke features. To better detect smaller smoke patterns in the images, the SiLU activation function is replaced with FReLU, a two-dimensional funnel-shaped activation function. This modification activates insensitive information in the network space while introducing minimal computational overhead and overfitting risks, thereby enhancing visual task performance. Experimental results demonstrate that the improved algorithm in this project exhibits a 6.8% increase in precision, a 2.8% increase in recall, and a 2.3% improvement in mean Average Precision relative to the original YOLOv5s network. This significant enhancement in detection accuracy makes it more suitable for practical smoke detection applications.