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For the requirements of all-weather environmental monitoring of smoke and fire emissions from coke plants, a coke oven smoke and fire recognition algorithm based on improved YOLOv5s is proposed; the algorithm uses YOLOv5s as the base network and adds CBAM"s attention mechanism module to the reference network, so that the network focuses more on relevant features and improves target detection accuracy; a new FReLU activation function replaces the SiLU activation function to improve the sensitivity of the activation space and improve the smoke and fire image vision task; on the basis of smoke and fire sample labels in the self-built dataset, add light labels to solve the interference of strong lights on flame recognition, and solve the smoke and fire detection problem of day and night scenes by shunting training and detection; do comparison experiments on the self-built dataset, after replacing the activation function, the joint CBAM module of the experimental results show, that the mAP value of smoke and fire detection in the day scene is improved by 6.7% compared with the original YOLOv5s model, and the mAP value of smoke and fire recognition in nighttime scenes is as high as 97.4%.