Enhancing YOLOv3-tiny for Mask Detection in Natural Scenes

With the worldwide spread of COVID-19, people's life safety has been greatly threatened. So, we consider using YOLOv3-tiny algorithm to detect mask wearing. Since there are few detection models for correctly wearing masks, we decided to use three classifications to detect correctly wearing masks, incorrectly wearing masks, and not wearing masks. Besides, in order to enhance the performance of our model in small object detection, we propose the k-means++ algorithm to make the size of the initial anchor boxes closer to the actual size of the object, and add a YOLO detection layer to effectively improve the accuracy of a small object. The results show that the mAP@50 values of our model are 4.68% higher than YOLOv3-tiny algorithm. Our model has significantly improved the detection ability of crowd scenes, and mask detection is more accurate and robust, which has good application value for mask detection in natural scenes.