{'fraction_non_alphanumeric': 0.013793103448275862, 'fraction_numerical': 0.008620689655172414, 'mean_word_length': 5.046875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Real-time Face Mask and Social Distancing Violation Detection System using YOLO

With the recent outbreak and rapid transmission of the COVID-19 pandemic, the need for the public to follow social distancing norms and wear masks in public is only increasing. According to the World Health Organization, to follow proper social distancing, people in public places must maintain at least 3ft or 1m distance between each other. This paper focuses on a solution to help enforce proper social distancing and wearing masks in public using YOLO object detection on video footage and images in real time. The experimental results shown in this paper infer that the detection of masked faces and human subjects based on YOLO has stronger robustness and faster detection speed as compared to its competitors. Our proposed object detection model achieved a mean average precision score of 94.75% with an inference speed of 38 FPS on video. The network ensures inference speed capable of delivering real-time results without compromising on accuracy, even in complex setups. The social distancing method proposed also yields promising results in several variable scenarios.