The detection algorithm can greatly improve the safety of construction workers under the background of construction sites and ensure the personal safety of construction workers. In this paper, the detection algorithm of helmet wearing is studied. YOLOv5 model is adopted as the core algorithm for personnel helmet wearing detection, and the lightweight model GHOST and attention mechanism CBAM are added to improve the original algorithm for construction scenarios. The lightweight model GHOST is a lightweight model used to reduce the computational load of convolutional neural networks. Attention mechanism CBAM used to amplify the receptive field of convolutional neural networks. Due to many dangerous factors, emergencies may occur at any time in the construction scene. Efficient and reliable personnel helmet wearing can further improve the operational efficiency and detection accuracy by using the attention mechanism. The algorithm has good performance in accuracy and robustness, and has high detection accuracy and good adaptability.