Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW

Abstract

An enhanced algorithm, YOLOv5s-FCW, is put forward in this study to tackle the problems that exist in the current helmet detection (HD) methods. These issues include having too many parameters, a complex network, and large computation requirements, making it unsuitable for deployment on embedded and other devices. Additionally, existing algorithms struggle with detecting small targets and do not achieve high enough recognition accuracy. Firstly, the YOLOv5s backbone network is replaced by FasterNet for feature extraction (FE), which reduces the number of parameters and computational effort in the network. Secondly, a convolutional block attention module (CBAM) is added to the YOLOv5 model to improve the detection model’s ability to detect small objects such as helmets by increasing its attention to them. Finally, to enhance model convergence, the WIoU_Loss loss function is adopted instead of the GIoU_Loss loss function. As reported by the experimental results, the YOLOv5s-FCW algorithm proposed in this study has improved accuracy by 4.6% compared to the baseline algorithm. The proposed approach not only enhances detection concerning small and obscured targets but also reduces computation for the YOLOv5s model by 20%, thereby decreasing the hardware cost while maintaining a higher average accuracy regarding detection.