Convolutional neural network-based crowd detection for COVID-19 social distancing protocol from unmanned aerial vehicles onboard camera

Abstract

Social distancing is a feasible solution to break the chain of the spread of coronavirus disease 2019 (COVID-19). A human crowd detection model was trained with a computational load that can be handled by a companion computer on the unmanned aerial vehicle (UAV) to minimize the spread of COVID-19. The model is designed to be able to measure social distance between people, whether it exceeds predetermined safe limits (1.5 m). The convolutional neural network model was trained using a dataset of 9600 images featuring humans, cyclists, and motorcyclists, with an allocation of 200 images each for testing and hyperparameter tuning. The image dataset was extracted from videos recorded above the UAV in the Institut Teknologi Bandung area, capturing diverse crowd scenarios throughout the day. The pre-trained model for transfer learning method is a single shot detector with MobileNet, ResNet50, and ResNet101 architectures. The measurement of the estimated social distance uses the Euclidian distance with the average Indonesian human as a reference, which is 1.6 m. MobileNet V2 was chosen as a crowd detection model with a lightweight size, which is only 19 MB and the average detection runtime for a single image is only 0.606s, in accordance with the load for the onboard companion computer. MobileNet V2 is also able to detect crowds of people well with the precision value reaching 84.9% and the recall value reaching 87.8%.