基于RISC-V架构嵌入式平台，使用卷积神经网络算法实现了一种口罩识别系统。通过比较目前常用的深度学习算法，选择使用TensorFlow平台、YOLO算法以及口罩识别模型，并对其进行适量的稀疏训练，来减少模型大小，使其可以在嵌入式平台上运行，再利用Dlib训练特征点检测模型，对截取到的人脸进行提取并保存特征值，达到实现人脸检测的目的。实验以LFW数据集为实验样本，通过实验证明该方法可以达到较高准确率。

This study uses convolutional neural network algorithm to implement a mask recognition system based on RISC-V architecture embedded platform. By comparing the current deep learning algorithms, we choose to use TensorFlow platform, YOLO algorithm and mask recognition model, and train them with appropriate amount of sparsity to reduce the model size so that they can run on the embedded platform. Then Dlib is adopted to train the feature point detection model to extract and save the feature values of the face, which can achieve the goal of recognizing faces. The LFW dataset is used as the experimental sample, and the experiment proves that the method can achieve high accuracy.