Face detection for rail transit passengers based on single shot detector and active learning.

COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.

SAS: A Platform of Spike Antigenicity for SARS-CoV-2.

Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.