An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic
Moein Razavi,Hamed Alikhani,Vahid Janfaza,Benyamin Sadeghi,Ehsan Alikhani +4 more
- Vol. 3, Iss: 1, pp 27-27
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TLDR
Wang et al. as mentioned in this paper developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the COVID-19 pandemic.Abstract:
The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people’s routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus’s spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle’s effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers’ safety in the pandemic situation.read more
Citations
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CNN-based bi-directional and directional long-short term memory network for determination of face mask
TL;DR: In this paper, a dataset consisting of 2000 images of a person from three different angles were collected in four classes, which are "masked", "non-masked", "masking but nose open" and'masked but under the chin".
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CNN-based bi-directional and directional long-short term memory network for determination of face mask
TL;DR: In this article , a dataset consisting of 2000 images was created to determine whether people are wearing the face mask correctly by using deep learning methods, and new models were proposed by transferring the learning through AlexNet and VGG16, which are the Convolutional Neural Network architectures.
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Face mask detection in COVID-19: a strategic review
TL;DR: In this paper , the need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis, and the pros and cons of each method have been analyzed in depth.
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A critical review of public–private partnerships in the COVID-19 pandemic: key themes and future research agenda
TL;DR: In this paper , the authors reviewed the current literature on PPPs in the COVID-19 pandemic and presented the key themes, research gaps, and future research directions.
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Feeding Material Identification for a Crusher Based on Deep Learning for Status Monitoring and Fault Diagnosis
TL;DR: In this paper , sound and vibration signals of the feeding materials are denoised by spectral subtraction and transformed into feature images by continuous wavelet transforms, and an image classification model based on CNN is built for these feature images to study its classification mechanism and performance.
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