Scaling up face masks detection with YOLO on a novel dataset

Face mask detection is a challenging research problem of computer vision because of the small sized area of face mask. The unavailability of proper datasets makes this problem even harder to crack. To address this bottleneck, we propose a novel face masks detection dataset consisting of 52,635 images with more than 50,000 tight bounding boxes and annotations for four different class labels namely, with masks, without masks, masks incorrectly, and mask area, which makes it a novel contribution for variety of face masks classification and detection tasks. Further, this dataset is tested with eight variants of the YOLO algorithm to determine its effectiveness. On the proposed dataset, original YOLO v4 achieved a mAP value of 71.69 % which was highest among all the original YOLO variants, tiny YOLO v4 achieved a mAP value of 57.71 % which was highest among all tiny variants. To propose new face masks detection algorithms that can perform with high accuracy in a limited computational resources environment, we selected four tiny variants of the YOLO algorithm and proposed new architectures modifications in their feature extraction networks that increased the overall performance and specifically, improved mAP by 4.12 % for tiny YOLO v3 and 2.54 % for tiny YOLO v4.

Automated summary

Researchers introduced a novel dataset for face mask detection that includes a large number of images and annotations, making significant contributions to various mask classification and detection tasks. Through testing with eight variants of the YOLO algorithm on the dataset, it was found that original YOLO v4 and tiny YOLO v4 performed the best.