Research on Helmet Wearing Detection Based on Improved YOLOv8 Algorithm

In response to the current challenges of low detection accuracy in traditional safety helmet detection network models, this paper introduces a novel model called C2f_Fastert_EMA_YOLOv8 based on YOLOv8. This innovative model design incorporates three key improvements aimed at enhancing detection performance and real-time capabilities. Firstly, we introduce the FasterBlock from FasterNet to replace certain Bottleneck components in the original C2f. This modification leads to the creation of the entirely new C2f_Faster module, significantly boosting the model's real-time detection speed, making it more suitable for rapid monitoring requirements in practical scenarios. Secondly, we incorporate an EMA attention mechanism module into the Neck part of the model. This module aids in capturing fine-grained details, enabling the model to focus more on training safety helmet-related target features and thereby enhancing detection accuracy. Lastly, we adopt the MPDIoU loss function to replace the original loss function. This improvement effectively enhances the model's bounding box regression performance, further strengthening detection accuracy. Through experiments conducted on the SHWD safety helmet dataset, we observed that the improved model achieved a 2.3% increase in the mean Average Precision (mAP) compared to the original model. Additionally, we successfully reduced the model's parameter size and overall size, reducing them by 2.62G and 1.5MB, respectively. This innovative model not only improves detection accuracy but also reduces model complexity, outperforming comparative algorithms and demonstrating significant potential for practical applications.