Social distance and face mask detector system exploiting transfer learning

Abstract

<p class="IJASEITAbtract"><span lang="EN-US">As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.</span></p>

Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance

“…Recent research presents diverse models for this purpose. In [5], MobileNetV2 and YOLOv3 achieved 99% accuracy for mask detection and 94% for social distancing. As seen in [6], hybrid approaches combining eigenfaces and neural networks attained test accuracies of 0.87, 0.987, and 0.989 for varying components.…”