A Deep Learning Based Novel Approach for Detection of Face Mask Wearing using Enhanced Single Shot Detector (SSD) over Convolutional Neural Network (CNN) with Improved Accuracy

Nithin., A; Jaisharma, K.

doi:10.1109/icbats54253.2022.9759018

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…is a very powerful algorithm. Being simple in design, the implementation is more direct from GPU and deep learning framework point of view and thus does the heavy lifting of detection at lightning speed (Nithin & Jaisharma, 2022).…”

Section: Single Shot Detector (Ssd)mentioning

confidence: 99%

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Iskandar¹,

Yel²,

Sitohang³

2022

JAETS

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Sign Language is a language that prioritizes manual communication, body language, and lip movements, instead of sound, to communicate. Deaf people are the main group who use this language, usually by combining hand shape, orientation and movement of the hands, arms, and body, and facial expressions to express their thoughts. Therefore, the researcher created an image recognition program in sign language using the Single Shot Detection (SSD) method, which is a convolution activity by combining several layers of preparation, by utilizing several components that move together and are motivated by a biological sensory system. The letters used in making sign language programs use the letters of the alphabet (az). This sign language detection programming that runs on the Google Collaboratory application

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Section: Single Shot Detector (Ssd)mentioning

confidence: 99%

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Iskandar¹,

Yel²,

Sitohang³

2022

JAETS

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show abstract

Section: Single Shot Detector (Ssd)mentioning

confidence: 99%

Sign Language Detection System Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Iskandar¹,

Yel²,

Maheswara³

2022

JAETS

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Sign language is a language that prioritizes communication with hands, body language, and lip movements to communicate. The deaf are the main group who use this language, often combining hand shape, hand, arm and body orientation and movement, and facial expressions to express their thoughts. The sign language detection system is designed using the Adaptive Neuro Fuzzy Inference System (ANFIS). This study uses data from the kaggle.com dataset, which is a site that provides research data on artificial intelligence. This study was conducted to recognize empty hand signals. Where it will help users naturally without any additional help. The test is carried out using a data set as evidenced by 1 display. In this process, The characteristics of the hand were carried out using the Histogram Oriented Gradient (HOG) method. Meanwhile, to separate it from the background image, it is used with color segmentation. The results of the process are then taken for classification. The classification process uses the Adaptive Neuro Fuzzy Inference System method. The results of the tests carried out for accuracy are as much as

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YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Cao

Zhao

et al. 2022

Biometric Recognition

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A Deep Learning Based Novel Approach for Detection of Face Mask Wearing using Enhanced Single Shot Detector (SSD) over Convolutional Neural Network (CNN) with Improved Accuracy

Cited by 4 publications

References 20 publications

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Sign Language Detection System Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

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