YOLOv5改进的轻量级口罩人脸检测

针对疫情防控下人脸识别应用出现人脸漏检、移动端平台的计算能力不足和硬件资源受限等问题,提出一种YOLOv5改进的轻量级口罩人脸检测模型.设计轻量化的C3Ghost模块替换原网络中的C3模块以压缩卷积过程的计算量和模型大小,在主干网络中添加注意力机制以提高网络的特征提取能力,并改进边框回归损失函数以提高检测速度和精度.实验结果表明,改进后的模型计算量和参数量分别降低了29.79%和33.33%,模型权重文件大小仅有2.8 M,减轻了对硬件条件的依赖,同时模型的检测率达到了96.6%,相比现有轻量级模型优势突出,能够有效地应用于人脸识别之中.

Improved Lightweight Masked Face Detection Based on YOLOv5

To address the problems of missed detection of faces, the insufficient computing power of mobile platforms, and the limited hardware resources of face recognition applications under epidemic prevention and control, this study proposes an improved lightweight detection model for faces with masks based on YOLOv5. In this model, the C3 module in the original network is replaced with a lightweight C3Ghost module to compress the computations of the convolution process and the size of the model. Moreover, an attention mechanism is added to the backbone network to improve the feature extraction capability of the network, and the border regression loss function is improved to improve the speed and accuracy of detection. The experimental results indicate that the amount of calculation and parameters of the improved model are decreased by 29.79% and 33.33%, respectively, with the weight file size of only 2.8 M. The improved model reduces the dependence on the hardware environment, and its detection rate reaches 96.6%. Compared with the existing models, it has outstanding advantages and can be effectively applied to face recognition.