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The MLX90614 and AMG8833 sensors were unable to detect remote temperature accurately. The Faster RCNN Inception model was better for my prototype, as it operated within a second, with 100% recall and 98% precision. On the hardware side, I calibrated 3 low-cost thermal sensors, MLX 90614, AMG8833 and FLIR Lepton 3.5 in a cardboard-blackbox to identify the best sensor for autonomous temperature detection, and tested how it measured my forehead temperature compared with a handheld thermometer. I evaluated two different neural networks, the SSD Mobilenet V1 and the Faster RCNN Inception, within Tensorflow, an open source artificial intelligence platform. The objective of this study was to identify the best algorithms and hardware solutions to build a low-cost automated COVID-19 screening system for small businesses that detects masks using artificial intelligence and measures forehead temperature using a thermal sensor. Employers are following CDC guidelines by creating mask mandates, requiring social distancing, and checking temperature at entrance. The COVID-19 Pandemic is our biggest challenge with already over 100 million infections and 2.4 million deaths globally. Prabhu, Yash (School: North Penn Senior High School) Autonomous COVID-19 Screening Using Deep Learning and Low-cost Thermal Imaging