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For power plants, smoking in the factory area is easy to cause fire, explosion and other safety hazards, and improper handling will bring huge losses. Aiming at the problem that the detection accuracy of illegal smoking behavior of personnel in power plant is not high, this paper proposed an improved YOLOv5s (You Only Look Once V5S) target detection method to detect illegal smoking behavior of personnel in power plant. Based on the object detection algorithm YOLOv5s, the 3×3 convolution in the C3 module of YOLOv5s network is replaced by a Bottleneck self-attention layer to improve the learning ability of the algorithm. The Efficient Channel Attention module (ECA) is added to the network to make the network pay more Attention to the target to be detected. At the same time, the loss function of YOLOv5s algorithm was changed to Scylla Intersection over Union (SIoU) to further improve the detection accuracy of the algorithm. Finally, the weighted Bidirectional Feature Pyramid Network (BiFPN) is used to replace the original YOLOv5s Feature Pyramid Network to rapidly perform multi-scale Feature fusion. The experimental results show that, compared with the traditional YOLOv5s algorithm, the improved algorithm improves the detection accuracy of smoking behavior, and the mAP (mean Average Precision) is increased by 2.2%. The recognition effect is significantly improved, which proves the effectiveness of the new algorithm.