Aiming at the problems of missing and false detection in existing smoke and fire detection algorithms, a new lightweight algorithm based on Effective Global Context Network (EGC-Net) is proposed. It takes the lightweight object detection network YOLOX as the basic network, and embeds an improved EGC-Net between the backbone feature extraction network and feature pyramid network of YOLOX. EGC-Net is composed of a three-stage structure of context modeling, feature transformation and feature fusion, which is used to obtain the global context information of image, model the long-range dependency of smoke or fire objects and its background, and learn more discriminative visual features by combining the channel attention mechanism for smoke and fire detection. Experimental results indicate that the image-level recall rate of the proposed smoke and fire detection algorithm EGC-YOLOX is 95.56%, and the image-level false alarm rate is 4.75%, both of which are superior to the compared typical lightweight algorithms, and the speed also meets the requirements of real-time detection. The proposed algorithm can be promoted and applied to the field of security and fire protection for real-time fire monitoring and early warning management.