全天候自然场景下的人脸佩戴口罩识别技术

Mask-wearing recognition in the wild

日常生活中,面对经呼吸道传播的传染性疾病或厂矿生产过程中产生的扬尘沙土,人们佩戴口罩进行防护可保护身体健康和生命安全.人脸佩戴口罩的自动化识别可以有效监督人们佩戴口罩,是抑制疾病快速传播和保护身体健康的重要技术手段.对于生活和生产中的口罩佩戴识别的需求,本文提出了基于深度学习的人脸检测和口罩佩戴识别相结合的方法.该方法在人脸检测中利用特征融合金字塔,结合空间和通道注意力学习,以及分割分支进行神经网络弱监督学习.另外针对检测后的人脸子图像,采用图像分类的方法实现快速识别,并加入注意力学习机制,增强模型对口罩区域特征的学习.利用近20万的公开和企业自有数据,并采用数据增强等方法,在全天候自然场景下取得了99.50%的识别准确率.该技术已广泛应用于滴滴出行实际业务中,日均处理百万数量级的请求.该服务已对外开放,关键算法已开源,从而使其发挥更大的应用价值和社会价值.

For public health and safety,wearing of masks is one of the most significant means to prevent infections.Additionally,masks protect employees of heavy industry from certain diseases during manufacture.To meet the demand of automatic mask-wearing recognition in scenes of life,we propose a recognition algorithm based on face detection and face attribute recognition.The face detection model not only adopted a fused feature pyramid and a spatial and channel attention mechanism but also a segmentation branch for weak supervision learning.Then for the detected face,we used classification for fast recognition.Moreover,we employed nearly 200000 images,attention mechanisms,data augmentation,and other techniques to enhance the robustness.Besides,this technology has been widely used in Didi Chuxing’s inspection systems and achieves 99.50%accuracy.Importantly,both the service and key algorithms have been opened to the public to maximize their social and application value.