To address the significantly elevated safety risks associated with construction workers' improper use of helmets and reflective clothing, we propose an enhanced YOLOv8 model tailored for safety wear detection. Firstly, this study introduces the P2 detection layer within the YOLOv8 architecture, which substantially enriches semantic feature representation. Additionally, a lightweight Ghost module is integrated to replace the original backbone of YOLOv8, thereby reducing the parameter count and computational burden. Moreover, we incorporate a Dynamic Head (Dyhead) that employs an attention mechanism to effectively extract features and spatial location information critical for site safety wear detection. This adaptation significantly enhances the model's representational power without adding computational overhead. Furthermore, we adopt an Exponential Moving Average (EMA) SlideLoss function, which not only boosts accuracy but also ensures the stability of our safety wear detection model's performance. Comparative evaluation of the experimental results indicates that our proposed model achieves a 6.2% improvement in mean Average Precision (mAP) compared to the baseline YOLOv8 model, while also increasing the detection speed by 55.88% in terms of frames per second (FPS).
Keywords: Deep learning; Ghost module; Site safety wearable detection; YOLOv8.
© 2024. The Author(s).