Abstract
Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.
Highlights
The current novel coronavirus pneumonia epidemic is raging around the world
Unmanned aerial vehicles (UAV) have the characteristics of flexible maneuverability, fast inspections, and high work efficiency, and have gradually formed an all-round three-dimensional inspection pattern of “air inspections–ground monitoring–communications, command, and control”, which plays an important role in improving the epidemic prevention and control systems and mechanisms, and in improving the efficiency of the national public health emergency management systems [3]
This paper describes a system of judging whether people are wearing masks that has been improved and optimized based on the single-shot multibox detector (SSD) algorithm
Summary
The current novel coronavirus pneumonia epidemic is raging around the world. As of 1 February 2021, the number of infections worldwide has exceeded 100 million, and the cumulative death toll has exceeded 2 million. If we can avoid crowd gatherings and if we can monitor personal contact and promptly remind the public to wear masks, we can effectively control and prevent the spread of the epidemic [2]. In the control and prevention of the novel coronavirus pneumonia epidemic, UAV high-altitude inspections, as an effective means of reducing the risk of contact and making up for the shortage of personnel for epidemic prevention and control, have become a powerful tool in the fight against the epidemic. (4) Based on intelligent voice warning technology, the system can avoid personal contact when reminding, dissuading, and publicizing the policy regarding face masks to ensure strong promotion of epidemic prevention and control.
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