Face Mask Detection using Artificial Intelligence for workplaces

Sharma, Ruchi; Sharma, Anupama; Jain, Raunak; Sharma, Soham; Singh, Sreshtha

doi:10.1109/iciccs53718.2022.9788188

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…For example, in the medical field, related identification applications include cervical cancer [ 13 ], blood cells [ 14 ], colorectal cancer [ 15 ], venipuncture [ 16 ], etc., Applications also include the auxiliaries for hearing and visually impaired people [ 17 ] and sign language identification [ 18 ]. Furthermore, due to the influence of epidemics, there were many mask identification studies based on the use of different algorithms [ 19 , 20 , 21 , 22 ]. There has been a lot of interest in self-driving cars such as automatic navigation [ 23 ], driver’s assistance [ 24 ], vehicle detection [ 25 ], vehicle tracking [ 26 ], and blind-spot detection [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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