万子伦, 张彦波, 王多峰, 孙怡晨, 谷沣洋, 陈明月. 复杂环境下多任务识别的人脸口罩检测算法[J]. 微电子学与计算机, 2021, 38(10): 21-27. DOI: 10.19304/J.ISSN1000-7180.2021.0056
引用本文: 万子伦, 张彦波, 王多峰, 孙怡晨, 谷沣洋, 陈明月. 复杂环境下多任务识别的人脸口罩检测算法[J]. 微电子学与计算机, 2021, 38(10): 21-27. DOI: 10.19304/J.ISSN1000-7180.2021.0056
WAN Zilun, ZHANG Yanbo, WANG Duofeng, SUN Yichen, GU Fengyang, CHEN Mingyue. Face mask detection algorithm for multi task recognition in complex environment[J]. Microelectronics & Computer, 2021, 38(10): 21-27. DOI: 10.19304/J.ISSN1000-7180.2021.0056
Citation: WAN Zilun, ZHANG Yanbo, WANG Duofeng, SUN Yichen, GU Fengyang, CHEN Mingyue. Face mask detection algorithm for multi task recognition in complex environment[J]. Microelectronics & Computer, 2021, 38(10): 21-27. DOI: 10.19304/J.ISSN1000-7180.2021.0056

复杂环境下多任务识别的人脸口罩检测算法

Face mask detection algorithm for multi task recognition in complex environment

  • 摘要: 疫情期间正确的佩戴口罩可以有效的防止病毒的传播,针对人员密集场所环境下往往存在复杂的干扰因素会导致对人脸佩戴口罩的检测任务产生影响,现有的基于Faster R-CNN检测算法无法满足复杂环境小目标的口罩佩戴检测,从而提出了一种基于改进Faster R-CNN的口罩佩戴检测算法,将传统的单一RPN网络模型改进使用多任务增强RPN模型以提高检测识别精度,利用改进的Soft-NMS算法删除区域候选网络输出的冗余候选框.同时使用了性能更好的CIoU损失函数替换了原本的IoU损失函数,充分考虑了目标与检测框之间的中心点距离,从而提高了检测的准确性将3000个样本图像按1 ∶3的正负样本数目比例对模型进行训练和实验验证.实验结果表明,与Faster R-CNN算法相比,本文算法分别在人脸检测精度和人脸口罩佩戴检测精度提高了7.15%和15.99%.其平均检测精度提升了11.57%,FPS提高了4.6.

     

    Abstract: The right wearing masks can effectively prevent the spread of viruses. There are usually complex distractions in densely populated environments, which will affect detection tasks of face masks. However, the current detecting algorithm based on Faster R-CNN can not meet mask wearing detection of small targets in complex environments, so this paper puts forward a mask wearing detection algorithm based on the improved Faster R-CNN. This algorithm changes the traditional single RPN network model into one multi-task enhanced RPN model, which can improve the detection and recognition accuracy, the improved Soft-NMS algorithm is used to delete the redundant candidate boxes of regional candidate network output. At the same time, CIoU loss function with better performance, which fully considers the center point distance between the target and the detection frame and improves the accuracy of detection, replaced the original IoU loss function. 3000 sample images are used to do training and experimental verification of the model according to 1:3ratio of positive and negative samples. Experimental results show that compared with the Faster R-CNN algorithm, the algorithm proposed in this paper improves the face detection accuracy and face mask wearing detection by 7.15% and 15.99%respectively. Besides, the average detection accuracy improved by 11.57% and the FPS by 4.6.

     

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