Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP

This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using B...

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Main Authors: Saiful Bahri, Iffah Zulaikha, Saon, Sharifah, Mahamad, Abd Kadir, Isa, Khalid, Fadlilah, Umi, Ahmadon, Mohd Anuaruddin, Yamaguchi, Shingo
Format: Article
Language:English
Published: Mdpi 2023
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Online Access:http://eprints.uthm.edu.my/11622/1/J16131_fcea86e7763277e20bdbfab7a92ccd21.pdf
http://eprints.uthm.edu.my/11622/
https://doi.org/10.3390/info14060319
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spelling my.uthm.eprints.116222024-09-25T07:21:22Z http://eprints.uthm.edu.my/11622/ Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP Saiful Bahri, Iffah Zulaikha Saon, Sharifah Mahamad, Abd Kadir Isa, Khalid Fadlilah, Umi Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo T Technology (General) This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using Bahasa Isyarat Malaysia (BIM). This project consists of three parts: First, we use BIM letters, which enables the recognition of BIM letters and BIM combined letters to form a word. In this part, a MobileNet pre-trained model is implemented to train the model with a total of 87,000 images for 29 classes, with a 10% test size and a 90% training size. The second part is BIM word hand gestures, which consists of five classes that are trained with the SSD-MobileNet-V2 FPNLite 320 × 320 pre-trained model with a speed of 22 s/frame rate and COCO mAP of 22.2, with a total of 500 images for all five classes and first-time training set to 2000 steps, while the second- and third-time training are set to 2500 steps. The third part is Android application development using Android Studio, which contains the features of the BIM letters and BIM word hand gestures, with the trained models converted into TensorFlow Lite. This feature also includes the conversion of speech to text, whereby this feature allows converting speech to text through the Android application. Thus, BIM letters obtain 99.75% accuracy after training the models, while BIM word hand gestures obtain 61.60% accuracy. The suggested system is validated as a result of these simulations and tests. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11622/1/J16131_fcea86e7763277e20bdbfab7a92ccd21.pdf Saiful Bahri, Iffah Zulaikha and Saon, Sharifah and Mahamad, Abd Kadir and Isa, Khalid and Fadlilah, Umi and Ahmadon, Mohd Anuaruddin and Yamaguchi, Shingo (2023) Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP. Information, 14 (319). pp. 1-20. https://doi.org/10.3390/info14060319
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Saiful Bahri, Iffah Zulaikha
Saon, Sharifah
Mahamad, Abd Kadir
Isa, Khalid
Fadlilah, Umi
Ahmadon, Mohd Anuaruddin
Yamaguchi, Shingo
Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
description This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using Bahasa Isyarat Malaysia (BIM). This project consists of three parts: First, we use BIM letters, which enables the recognition of BIM letters and BIM combined letters to form a word. In this part, a MobileNet pre-trained model is implemented to train the model with a total of 87,000 images for 29 classes, with a 10% test size and a 90% training size. The second part is BIM word hand gestures, which consists of five classes that are trained with the SSD-MobileNet-V2 FPNLite 320 × 320 pre-trained model with a speed of 22 s/frame rate and COCO mAP of 22.2, with a total of 500 images for all five classes and first-time training set to 2000 steps, while the second- and third-time training are set to 2500 steps. The third part is Android application development using Android Studio, which contains the features of the BIM letters and BIM word hand gestures, with the trained models converted into TensorFlow Lite. This feature also includes the conversion of speech to text, whereby this feature allows converting speech to text through the Android application. Thus, BIM letters obtain 99.75% accuracy after training the models, while BIM word hand gestures obtain 61.60% accuracy. The suggested system is validated as a result of these simulations and tests.
format Article
author Saiful Bahri, Iffah Zulaikha
Saon, Sharifah
Mahamad, Abd Kadir
Isa, Khalid
Fadlilah, Umi
Ahmadon, Mohd Anuaruddin
Yamaguchi, Shingo
author_facet Saiful Bahri, Iffah Zulaikha
Saon, Sharifah
Mahamad, Abd Kadir
Isa, Khalid
Fadlilah, Umi
Ahmadon, Mohd Anuaruddin
Yamaguchi, Shingo
author_sort Saiful Bahri, Iffah Zulaikha
title Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
title_short Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
title_full Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
title_fullStr Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
title_full_unstemmed Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP
title_sort interpretation of bahasa isyarat malaysia (bim) using ssd-mobilenet-v2 fpnlite and coco map
publisher Mdpi
publishDate 2023
url http://eprints.uthm.edu.my/11622/1/J16131_fcea86e7763277e20bdbfab7a92ccd21.pdf
http://eprints.uthm.edu.my/11622/
https://doi.org/10.3390/info14060319
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score 13.214268