Objective In response to the issues of low detection accuracy and poor performance of safety helmet detection in complex industrial environments a method for intelligent detection of safety helmets based on YOLOv5 was proposed. Methods First an attention mechanism was incorporated into the backbone network of the original YOLOv5 model which enhanced the extraction of features for targets of different sizes. This modification directed the network?? s attention toward regions containing safety helmets thereby improving the network?? s ability to capture safety helmet information and effectively extract the corresponding features. In the prediction layer the EIoU loss function was employed which considered the differences in width and confidence as well as height and confidence while splitting the aspect ratio. This approach addressed the issue of sample imbalance accelerated convergence and enhanced regression accuracy. Results According to experimental results the improved algorithm achieved an average precision of 94. 7%. The improved algorithm improved the average detection accuracy by 2. 2% compared with the YOLOv5 algorithm and 12. 6% compared with the YOLOv3 algorithm effectively detecting safety helmets. Conclusion Under the same background environment the improved algorithm can effectively detect small targets at long distances and accurately detect targets in images with complex background information. The improved algorithm effectively addresses the issues of missed detection and false detection of small targets in the original algorithm and also improves detection accuracy.