摘要： 新型冠状病毒肺炎疫情严重威胁人们的生命安全，对于聚集性人群密度及口罩佩戴情况的监管是控制病毒扩散的重要途经。公共场所具有人流密集且流动性大的特点，人工监测易增加感染风险，而现有基于深度学习的口罩检测算法存在功能及场景单一的问题，不能在多场景下实现多类别检测，同时精度也有待提升。提出Cascade-Attention R-CNN目标检测算法，实现对聚集区域、行人和口罩佩戴情况的自动检测。针对任务中目标尺度变化过大的问题，选取高精度两阶段Cascade R-CNN目标检测算法作为基础检测框架。通过设计多个级联的候选分类-回归网络并加入空间注意力机制，突出候选区域特征中的重要特征并抑制噪声特征，从而提高检测精度。在此基础上，构建聚集性传染风险智能监测模型，结合Cascade-Attention R-CNN算法的输出结果确定传染风险等级。实验结果表明，该模型对于不同场景和视角的多类别目标图片具有较高的准确性和鲁棒性，Cascade-Attention R-CNN算法平均精度均值达到89.4%，较原始Cascade RCNN算法提升2.6个百分点，较经典的两阶段目标检测算法Faster R-CNN和单阶段目标检测框架RetinaNet分别提升10.1和8.4个百分点。

关键词: 新冠肺炎疫情防控, 聚集性传染风险, 智能监测模型, 目标检测, Cascade R-CNN算法

Abstract: The Corona Virus Disease 2019(COVID-19) epidemic is a serious threat to people's lives.Supervision of the density of clustered people and wearing of masks is key to controlling the virus.Public places are characterized by a dense flow of people and high mobility.Manual monitoring can easily increase the risk of infection, and existing mask detection algorithms based on deep learning suffer from the limitation of having a single function and can be applied to only a single type of scenes; as such, they cannot achieve multi-category detection across multiple scenes.Furthermore, their accuracy needs to be improved.The Cascade-Attention R-CNN target detection algorithm is proposed for realizing the automatic detection of aggregations in areas, pedestrians, and face masks.Aiming to solve the problem that the target scale changes too significantly during the task, a high-precision two-stage Cascade R-CNN target detection algorithm is selected as the basic detection framework.By designing multiple cascaded candidate classification regression networks and adding a spatial attention mechanism, we highlight the important features of the candidate region features and suppress noise features to improve the detection accuracy.Based on this, an intelligent monitoring model for aggregated infection risk is constructed, and the infection risk level is determined by combining the outputs of the proposed algorithm.The experimental results show that the model has high accuracy and robustness for multi-category target images with different scenes and perspectives.The average accuracy of the Cascade Attention R-CNN algorithm reaches 89.4%, which is 2.6 percentage points higher than that of the original Cascade R-CNN algorithm, and 10.1 and 8.4 percentage points higher than those of the classic two-stage target detection algorithm, Faster R-CNN and the single-stage target detection framework, RetinaNet, respectively.

Key words: Corona Virus Disease 2019(COVID-19) epidemic prevention and control, aggregated infection risk, intelligent monitoring model, object detection, Cascade R-CNN algorithm

中图分类号: 

• R183.3