Objective To address the issues of large model parameters long inference time and high rates of false positives and negatives in safety helmet detection in substation scenarios a real-time helmet-wearing detection algorithm YOLO-FE BCKD based on knowledge distillation and attention mechanism is proposed. Methods In this model to reduce the model size and inference time FasterNet blocks were used to replace C2f modules in the original backbone network and NECK section. Furthermore to mitigate the significant drop in accuracy due to reduced model parameters an efficient multi-scale EMA attention mechanism was integrated into the backbone network to enhance the model?? s feature extraction capability. Finally to achieve higher accuracy YOLOv8m was selected as the teacher network and YOLO-FE as the student network and the BCKD knowledge distillation method was adopted to distill knowledge into YOLO-FE. Results This study used a helmet dataset collected from coal mine substations and conducted comparative experiments with different algorithms. The results showed that the improved YOLOv8 model achieved an average precision of 74. 5% before distillation surpassing YOLOv8n by 2. 5% with a 26. 7% reduction in parameters and a 26% increase in detection speed. After distillation the model?? s average precision reached 75. 4%. To verify the algorithm?? s generalizability it was tested on the public dataset GDUT-HWD achieving an accuracy of 84. 2% a 3% improvement over the original model. Conclusion The proposed helmet-wearing detection algorithm achieves a good balance between accuracy and real-time performance meeting the requirements for real-time detection in substation environments.