YOLO Based Thermal Screening Using Artificial Intelligence (Al) for Instinctive Human Facial Detection

Covid19 remains the world's greatest public health emergency. It has become indispensable to measure the temperature of people entering or leaving croweded places to ease the identification of potentially infected and to isolate them from spreading and preventing the spread of the ongoing global pandemic of coronavirus disease. This research work is focusing on thermal screening for an automated scanner using Artificial Intelligence (AI) for instinctive temperature measurement on human faces. The framework used for facial detection is known as YOLOv5 which is a family of compound-scaled object detection models trained on the COCO, a large-scale object detection, segmentation, and captioning dataset. YOLOv5 is able to detect several different objects simultaneously by using its available pre-trained models and robustness of detecting faces even at the vicinity of face masks. The research presents the application, training procedure and capability of the Yolov5. This system is not only used for the human face detection, but also for the detection of some commonly-used objects as an extension to its overall application and performance. Yolov5 is readily available to be implemented in Python, the core programming language working under an Ubuntu-based Operating System providing users the best experience. One of the important outcomes of this research work is the development of a Graphical User Interface (GUI) to work alongside the main programme flow.