YOLOv7-WFD: A Novel Convolutional Neural Network Model for Helmet Detection in High-Risk Workplaces
Jianjun Chen, Junning Zhu, Zhuang Li, Xibei Yang
Abstract
In the construction industry, it is common occurrence for head injuries caused by workers not wearing a helmet. However, the current models for detecting safety helmet either have insufficient detection accuracy or insufficient generalization ability. For this reason, an improved convolutional neural network model, called YOLOv7-WFD, is proposed for the detection of workers without helmets in this paper. Firstly, a new module called DBS in this paper is proposed to strengthen the ability of model to extract target features. This module consists of a Deformable Convolutional, a Batch Normalization layer and a SiLU activation function. Secondly, the Content-Aware ReAssembly of Features (CARAFE) module is introduced to perceive effective features, which improves the model’s ability to reconstruct details and structural information during image up-sampling. Thirdly, Wise-IoU, which is a loss function with dynamic focusing mechanism, is adopted as the loss function to calculate localization loss, which enhances the generalization capability of model and accuracy of detection. Wise-IoU also can evaluate the "outlier" of the anchor box quality, and attenuate the negative impact of low-quality samples in the dataset and enhance the generalization ability of the model. Finally, the experiment shows that the improved YOLOv7-WFD achieves a mAP of 92.6% and a FPS of 79.3 when tested on SHEL5K dataset.