Edge-enhanced instance segmentation by grid regions of interest.

This paper focuses on the instance segmentation task. The purpose of instance segmentation is to jointly detect, classify and segment individual instances in images, so it is used to solve a large number of industrial tasks such as novel coronavirus diagnosis and autonomous driving. However, it is not easy for instance models to achieve good results in terms of both efficiency of prediction classes and segmentation results of instance edges. We propose a single-stage instance segmentation model EEMask (edge-enhanced mask), which generates grid ROIs (regions of interest) instead of proposal boxes. EEMask divides the image uniformly according to the grid and then calculates the relevance between the grids based on the distance and grayscale values. Finally, EEMask uses the grid relevance to generate grid ROIs and grid classes. In addition, we design an edge-enhanced layer, which enhances the model's ability to perceive instance edges by increasing the number of channels with higher contrast at the instance edges. There is not any additional convolutional layer overhead, so the whole process is efficient. We evaluate EEMask on a public benchmark. On average, EEMask is 17.8% faster than BlendMask with the same training schedule. EEMask achieves a mask AP score of 39.9 on the MS COCO dataset, which outperforms Mask RCNN by 7.5% and BlendMask by 3.9%.

Single-Stage Real-Time Face Mask Detection

With the battle against COVID-19 entering a more intense stage against the new Omicron variant, the study of face mask detection technologies has become highly regarded in the research community. While there were many works published on this matter, we still noticed three research gaps that our contributions could possibly suffice. Firstly, despite the introduction of various mask detectors over the last two years, most of them were constructed following the two-stage approach and are inappropriate for usage in real-time applications The second gap is how the currently available datasets could not support the detectors in identifying correct, incorrect and no mask-wearing efficiently without the need for data pre-processing. The third and final gap concerns the costly expenses required as the other detector models were embedded into microcomputers such as Arduino and Raspberry Pi. In this paper, we will first propose a modified YOLO-based model that was explicitly designed to resolve the real-time face mask detection problem;during the process, we have updated the collected datasets and thus will also make them publicly available so that other similar experiments could benefit from;lastly, the proposed model is then implemented onto our custom web application for real-time face mask detection. Our resulted model was shown to exceed its baseline on the revised dataset, and its performance when applied to the application was satisfactory with insignificant inference time. Code available at: https://bitbucket.org/indigoYoshimaru/facemask-web

Development of Face Mask Detection using SSDLite MobilenetV3 Small on Raspberry Pi 4

This study aimed to develop a mask detection tool with SSDLite MobilenetV3 Small based on Raspberry Pi 4. SSDLite MobilenetV3 Small is a single-stage object detection. The single-stage object detection method is faster than the two-stage detection method. However, it has the disadvantage as the level of accuracy is not as good as the two-stage detection method. In the experiments, we used some methods to compare with SSDLite MobilenetV3, such as: SSDLite MobilenetV3 Large, SSDLite MobilenetV2, SSD MobilenetV2, SSDLite Mobileedets, and SSDMNV2 models. The result is that SSDLite MobilenetV3 is more powerful than other systems for detecting face masks. While the model with the best detection is the SSDLite MobilenetV2 model, the system with the SSDLite MobilenetV3 Small model still detects the use of masks, with a score of 70% accuracy from model accuracy testing in deployment. The limitation is the system with SSDLite MobilenetV3 Small can't detect incorrect masks. © 2022 IEEE.

Single-Stage Real-Time Face Mask Detection

With the battle against COVID-19 entering a more intense stage against the new Omicron variant, the study of face mask detection technologies has become highly regarded in the research community. While there were many works published on this matter, we still noticed three research gaps that our contributions could possibly suffice. Firstly, despite the introduction of various mask detectors over the last two years, most of them were constructed following the two-stage approach and are inappropriate for usage in real-time applications The second gap is how the currently available datasets could not support the detectors in identifying correct, incorrect and no mask-wearing efficiently without the need for data pre-processing. The third and final gap concerns the costly expenses required as the other detector models were embedded into microcomputers such as Arduino and Raspberry Pi. In this paper, we will first propose a modified YOLO-based model that was explicitly designed to resolve the real-time face mask detection problem;during the process, we have updated the collected datasets and thus will also make them publicly available so that other similar experiments could benefit from;lastly, the proposed model is then implemented onto our custom web application for real-time face mask detection. Our resulted model was shown to exceed its baseline on the revised dataset, and its performance when applied to the application was satisfactory with insignificant inference time. Code available at: https://bitbucket.org/indigoYoshimaru/facemask-web © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Optimizing two-stage surgery by expanding single-stage nasal reconstruction in the COVID-19 era.

The COVID-19 pandemic led to a decrease in the number of operating rooms available. Single-stage islanded forehead flaps have emerged as a good alternative to the classic frontal flap helping to diminish the surgical waiting list. We present our case series of 6 patients reconstructed with islanded forehead flaps between February and July 2020.The purpose of this report is to assess the advantages and disadvantages of this technique in order to inform which subgroup of patients may benefit from the one-stage flap, now the pandemic is better controlled.

RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model. © 2021 IEEE.