Improving Health and Safety Promotion with a Robotic Tool

M., Sergio D. Sierra; Insuasty, Maria; A., Daniel E. Garcia; Múnera, Marcela; Cifuentes, Carlos A.

doi:10.1145/3568294.3580180

Cited by 1 publication

(4 citation statements)

References 2 publications

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“…Only a handful of mask detection methods have been developed for use by robots and their onboard cameras [15][16][17][18][19][20].…”

Section: Autonomous Mask Detection By Robotsmentioning

confidence: 99%

“…In [17], the NAO robot was used to promote face mask usage in public spaces during the pandemic. The robot used an external webcam and a single shot detector (SSD) with a ResNet-10 architecture as its backbone for mask detection.…”

Section: Autonomous Mask Detection By Robotsmentioning

confidence: 99%

“…Many mask detection methods use high-resolution images while training and testing their models; however, robots have cameras that provide lower-resolution images (e.g., Pepper's onboard camera has a 640 × 480 pixel image resolution with a maximum signal-to-noise ratio of 6 dB [60]), which may cause detection and classification errors. To date, to the authors' knowledge, there are only three mask detection systems integrated into real robots; however, they only provide binary mask classification in real-world settings [15,17,20].…”

Section: Summary Of Limitationsmentioning

confidence: 99%

“…To enforce the mandatory wearing of face masks while reducing the time-consuming and costly task for humans to check people as they enter buildings, outdoor venues, etc., automated monitoring systems have been integrated into security cameras [13], tablets [14], and robots [15][16][17][18][19][20]. Recent detection systems have also used deep learning to classify whether people are wearing or not wearing masks (binary classification) in 2D images and videos [21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Robust Face Mask Detection by a Socially Assistive Robot Using Deep Learning

Zhang,

Effati,

Tan

et al. 2023

Computers

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Wearing masks in indoor and outdoor public places has been mandatory in a number of countries during the COVID-19 pandemic. Correctly wearing a face mask can reduce the transmission of the virus through respiratory droplets. In this paper, a novel two-step deep learning (DL) method based on our extended ResNet-50 is presented. It can detect and classify whether face masks are missing, are worn correctly or incorrectly, or the face is covered by other means (e.g., a hand or hair). Our DL method utilizes transfer learning with pretrained ResNet-50 weights to reduce training time and increase detection accuracy. Training and validation are achieved using the MaskedFace-Net, MAsked FAces (MAFA), and CelebA datasets. The trained model has been incorporated onto a socially assistive robot for robust and autonomous detection by a robot using lower-resolution images from the onboard camera. The results show a classification accuracy of 84.13% for the classification of no mask, correctly masked, and incorrectly masked faces in various real-world poses and occlusion scenarios using the robot.

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“…Only a handful of mask detection methods have been developed for use by robots and their onboard cameras [15][16][17][18][19][20].…”

Section: Autonomous Mask Detection By Robotsmentioning

confidence: 99%

Section: Autonomous Mask Detection By Robotsmentioning

confidence: 99%