Face mask detection application and dataset

Matthias, Daniel; Managwu, Chidozie; Olumide, O.

doi:10.4314/jcsia.v27i2.5

Cited by 4 publications

(4 citation statements)

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“…The first step in obtaining a face-mask detection system is the image collection. Images with/without masks are involved in the dataset [17], [18]. Images of 4,095 masked and unmasked individuals are used to create this dataset each of which is labelled, tagged, and pre-processed before training and testing.…”

Section: Datasetmentioning

confidence: 99%

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

Abed

Al-Ibadi²,

Abed³

2023

Bulletin EEI

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COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.

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Section: Datasetmentioning

confidence: 99%

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

Abed

Al-Ibadi²,

Abed³

2023

Bulletin EEI

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“…To create a dataset, images from an open source [12] are used. Applying data improvement techniques can increase the size of datasets.…”

Section: Data Collectionmentioning

confidence: 99%

Real-Time Face Mask Detection Using Deep Learning

Robin,

Islam,

Rasel

et al. 2023

IJAIS

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COVID-19 has made a severe impact on the world's economy, health, education sector, and so on. Although the infection rate of the COVID-19 virus has dropped significantly in recent days, there is no scope to ignore precautionary measures like wearing masks in public. In this paper we proposed, a real-time Deep Learning based face detection system using a balanced dataset of over 6000 images divided into two classes named 'With Mask' and 'Without Mask. The existing studies do not consider the data where people wear masks but not correctly, hence violating the safety measures. Our dataset is well-curated to detect people who do not wear masks properly. Our proposed CNN model not only overperformed other existing studies in terms of accuracy but also requires significantly less memory and time compared to other existing models. Our model achieved an accuracy rate of about 98.5% and it requires only 20 Mega Bytes of memory to deploy the model.

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“…Their model achieved an accuracy equal to 93.64%. Matthias et al [27] proposed an FMD model. They used a dataset that was used to detect the main facial features such as the nose, eyes, and mouth.…”

Section: Introductionmentioning

confidence: 99%

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

Benifa

Chola

Muaad

et al. 2023

Sensors

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A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

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Face mask detection application and dataset

Cited by 4 publications

References 0 publications

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

Real-Time Face Mask Detection Using Deep Learning

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

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