College of Automation, Chongqing University of Post and Telecommunications, Chongqing 400065, China.
Key Lab of Industrial Wireless Networks and Networked Control of the Ministry of Education, Chongqing 400065, China.
Sensors (Basel). 2021 May 8;21(9):3263. doi: 10.3390/s21093263.
To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.
为了解决复杂环境下准确率低、实时性差、鲁棒性差等问题，本文提出了一种基于改进 YOLO-v4 的口罩识别与规范佩戴检测算法。首先，在主干特征提取网络中引入改进的 CSPDarkNet53，降低了网络的计算成本，提高了模型的学习能力。其次，自适应图像缩放算法可以有效减少计算量和冗余。再次，引入改进的 PANet 结构，使网络在特征层具有更多的语义信息。最后，根据口罩规范佩戴制作口罩检测数据集，基于深度学习的目标检测算法，通过多种评价指标来评估模型的有效性。对比结果表明，口罩识别的 mAP 可以达到 98.3%，帧率高达 54.57FPS，与现有算法相比更加准确。
