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Aiming at the problems of difficult detection of occluded objects and high false detection and missed detection rate in the current helmet detection methods on construction sites, an improved YOLOv5 helmet detection method is proposed in this paper. First, use the K-means++ clustering algorithm to redesign the prior anchor box size to match the helmet dataset. Second, Swin Transformer is used as the backbone network of YOLOv5 to extract features. The multi-head self-attention mechanism based on shiftable windows can model the dependencies between different spatial location features, effectively capture global context information, and have better Feature extraction capability. Third, the C3-Ghost module is proposed, which improves the C3 module of YOLOv5 based on Ghost Bottleneck, aiming to generate more valuable redundant feature maps through low-cost operations, effectively reducing model parameters and computational complexity. Fourth, a new feature fusion module is proposed based on the structural advantages of cross-scale feature fusion of bidirectional feature pyramid network, which can better adapt to target detection tasks of different scales. The experimental results show that compared with the original YOLOv5, the mAP@.5:.95 index of the improved YOLOv5 on the helmet detection task is improved by 2.3 percentage points, which meets the accuracy requirements of helmet wearing detection in complex construction scenarios.