Intelligent Monitoring Model for Aggregated Infection Risk Against the Background of COVID-19 Epidemic

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.