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Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2021-02-16 , DOI: 10.1142/s0218001421550077
Guohua Liu 1, 2 , Qintao Zhang 1
Affiliation  

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on k-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.

中文翻译:

基于改进Tiny YOLOv3的口罩佩戴检测算法

新型冠状病毒通过飞沫、气溶胶和其他载体广泛传播。佩戴口罩可以有效降低被病毒感染的概率。因此,有必要监测人们在公共场所是否戴口罩,以防止病毒进一步传播。但是,目前还没有成熟的通用口罩佩戴检测算法。本文基于tiny YOLOv3算法,实现了戴口罩人脸和不戴口罩人脸的检测,并对算法提出改进。首先,优化边界框回归的损失函数,将原始损失函数优化为Generalized Intersection over Union (GIoU)损失。二、网络结构改进,将残差单元引入主干以增加网络的深度,并将两个尺度的检测扩展到三个。最后,anchor box的大小根据ķ-意味着算法。在构建的数据集上的实验结果表明,与tiny YOLOv3算法相比,本文提出的算法在保持高速推理能力的同时提高了检测精度。
更新日期:2021-02-16
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