The continuous development of smart industrial parks has imposed increasingly stringent requirements on safety helmet detection in environments such as factories, construction sites, rail transit, and fire protection. Current models often suffer from issues like false alarms or missed detections, especially when dealing with small and densely packed targets. This study aims to enhance the YOLOv5 target detection method to provide real-time alerts for individuals not wearing safety helmets in complex scenarios. Our approach involves incorporating the ECA channel attention mechanism into the YOLOv5 backbone network, allowing for efficient feature extraction while reducing computational load. We adopt a weighted bi-directional feature pyramid network structure (BiFPN) to facilitate effective feature fusion and cross-scale information transmission. Additionally, the introduction of a decoupling head in YOLOv5 improves detection performance and convergence rate. The experimental results demonstrate a substantial improvement in the YOLOv5 model's performance. The enhanced YOLOv5 model achieved an average accuracy of 95.9% on a custom-made helmet dataset, a 3.0 percentage point increase compared to the original YOLOv5 model. This study holds significant implications for enhancing the accuracy and robustness of helmet-wearing detection in various settings.