In view of the existing monitoring system’s inability to cope with the problems of cross-infection and traceability difficulties in the epidemic environment， a design scheme for a public transportation detection system based on edge computing was proposed. Firstly， a graph database was established to store passengers and ride information， and at the same time a dual database model was used to prevent the blockage caused by building index， thereby achieving the balance between insertion efficiency and search efficiency. Then， in the extraction of vehicle and human image information， the HSV （Hue Saturation Value） color space was used to preprocess the image， and a three-dimensional space model of face was established to improve the recognition accuracy of the neural network. When the object wore a mask， the feature point information was able to be regressed through the obvious nose tip feature points， lower jaw feature points， and unobstructed nose bridge feature points. Finally， k-hop search was used to find close contacts quickly. In the feature comparison test， the correct rates of this model are 99.44% and 99.23% on BioID dataset and PubFig dataset， respectively， and the false negative rates of the model on the two datasets are both less than 0.01%. In the graph search efficiency test， there is no big difference between the graph database and the relational database when searching at a shallow level. When the search level becomes deeper， the graph database is more efficient. After verifying the theoretical feasibility， the actual environment of buses and bus stops was simulated. In the test， the proposed system has the recognition accuracy of 99.98%， and the average recognition time of about 21 ms， which meets the requirements of epidemic monitoring. The proposed system design can meet the special needs of public safety during the epidemic period， and can realize the functions of person recognition， route recording， and potential contact search， which can effectively ensure public transportation safety.