Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin

The American Sign Language (ASL) is a nonverbal communication language that uses visual sign patterns formed with the hands or any part of the body and is usually utilized by persons who have hearing or listening disabilities. The deaf and mute people have difficulty communicating their thoughts, ne...

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Main Author:	Nasharudin, Muhammad Imran
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Applied psychology
Online Access:	https://ir.uitm.edu.my/id/eprint/89043/1/89043.pdf
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id	my-uitm-ir.89043
record_format	uketd_dc
spelling	my-uitm-ir.890432024-03-19T07:08:33Z Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin 2023 Nasharudin, Muhammad Imran Applied psychology The American Sign Language (ASL) is a nonverbal communication language that uses visual sign patterns formed with the hands or any part of the body and is usually utilized by persons who have hearing or listening disabilities. The deaf and mute people have difficulty communicating their thoughts, needs, and feelings through spoken language. There is a need to have an alternative method based on computer technology for those who are deafen or hard of hearing people since vocal communication is not available to them. One of the issues of computer-based sign language recognition is latency which creates delay in executing the interpretation of certain gestures. To balance latency vs. throughput, the architecture is discovered automatically using a Neural Architecture Search (NAS) technology called AutoNAC. The innovative features of YOLO-NAS include the quantization aware modules QSP and QCI, which combine re-parameterization for 8-bit quantization to minimize accuracy loss during post-training quantization. The architecture is designed to identify tiny objects, increase localization accuracy, and improve the performance-per-compute ratio, making it appropriate for real-time edge-device applications. As using YOLO-NAS for the sign language recognition, we succeeded to deploy average of detection percentage of 86% of all sign language alphabets. YOLO-NAS networks are successfully used in sign language recognition, with a reported 96.41 (mAP@50). 2023 Thesis https://ir.uitm.edu.my/id/eprint/89043/ https://ir.uitm.edu.my/id/eprint/89043/1/89043.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Applied psychology
spellingShingle	Applied psychology Nasharudin, Muhammad Imran Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
description	The American Sign Language (ASL) is a nonverbal communication language that uses visual sign patterns formed with the hands or any part of the body and is usually utilized by persons who have hearing or listening disabilities. The deaf and mute people have difficulty communicating their thoughts, needs, and feelings through spoken language. There is a need to have an alternative method based on computer technology for those who are deafen or hard of hearing people since vocal communication is not available to them. One of the issues of computer-based sign language recognition is latency which creates delay in executing the interpretation of certain gestures. To balance latency vs. throughput, the architecture is discovered automatically using a Neural Architecture Search (NAS) technology called AutoNAC. The innovative features of YOLO-NAS include the quantization aware modules QSP and QCI, which combine re-parameterization for 8-bit quantization to minimize accuracy loss during post-training quantization. The architecture is designed to identify tiny objects, increase localization accuracy, and improve the performance-per-compute ratio, making it appropriate for real-time edge-device applications. As using YOLO-NAS for the sign language recognition, we succeeded to deploy average of detection percentage of 86% of all sign language alphabets. YOLO-NAS networks are successfully used in sign language recognition, with a reported 96.41 (mAP@50).
format	Thesis
qualification_level	Bachelor degree
author	Nasharudin, Muhammad Imran
author_facet	Nasharudin, Muhammad Imran
author_sort	Nasharudin, Muhammad Imran
title	Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
title_short	Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
title_full	Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
title_fullStr	Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
title_full_unstemmed	Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin
title_sort	sign language recognition using you only look once-neural architecture search / muhammad imran nasharudin
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89043/1/89043.pdf
_version_	1794192192672104448

Sign language recognition using You Only Look Once-Neural Architecture Search / Muhammad Imran Nasharudin

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