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Law offenders take advantage of face masks to conceal their identities and in the present time of the COVID-19 pandemic wearing face masks is a new norm which makes it a daunting task for the investigation agencies to identify the offenders. To address the issue of detection of people wearing face masks using surveillance cameras, we propose a novel face mask vision system that is based on an improved tiny YOLO v4 object detector. The face masks detection network of the proposed vision system is developed by integrating tiny YOLO v4 with spatial pyramid pooling (SPP) module and additional YOLO detection layer and tested and validated on a self-created face masks detection dataset consisting of more than 50,000 images. The proposed tiny YOLO v4-SPP network achieved a mAP (mean average precision) value of 64.31% on the employed dataset which was 6.6% higher than tiny YOLO v4. Specifically, for detection of the presence of a small object like a face mask on the face region, the proposed tiny YOLO v4-SPP based vision system achieved an AP (average precision) of 84.42% which was 14.05% higher than the original tiny YOLO v4 thus, ensuring that the proposed network is capable of accurate detection of a mask on the face region in real-time surveillance applications where visibility of complete face area is a guideline.

A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system