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.

中文翻译：

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基于改进YOLO-v4的防毒面具佩戴检测算法

针对复杂环境导致的精度低，实时性差，鲁棒性差等问题，提出了一种基于改进的YOLO-v4的人脸识别和标准磨损检测算法。首先，将改进的CSPDarkNet53引入到主干特征提取网络中，从而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地减少计算量和冗余度。第三，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，根据口罩的标准佩戴量建立口罩检测数据集。基于深度学习的目标检测算法，比较各种评估指标以评估模型的有效性。比较结果表明，面罩识别的mAP可以达到98.3％，帧率高达54.57 FPS，与现有算法相比，精度更高。