Color recognition wearable device using machine learning for visually impaired person

Bolad, Tarek Mohamed and Nik Hashim, Nik Nur Wahidah and Mohamad Hanif, Noor Hazrin Hany (2018) Color recognition wearable device using machine learning for visually impaired person. IIUM Engineering Journal, 19 (2). pp. 213-220. ISSN 1511-788X E-ISSN 2289-7860

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Abstract

Recognizing colors is a concerning problem for the visually impaired person. The aim of this paper is to convert colors to sound and vibration in order to allow fully/partially blind people to have a ‘feeling’ or better understanding of the different colors around them. The idea is to develop a device that can produce vibration for colors. The user can also hear the name of the color along with ‘feeling’ the vibration. Two algorithms were used to distinguish between colors; RGB to HSV color conversion in comparison with neural network and decision tree based machine learning algorithms. Raspberry Pi 3 with Open Source Computer Vision (OpenCV) software handles the image processing. The results for RGB to HSV color conversion algorithm were performed with 3 different colors (red, blue, and green). In addition, neural network and decision tree algorithms were trained and tested with eight colors (red, green, blue, orange, yellow, purple, white, and black) for the conversion to sound and vibration. Neural network and decision tree algorithms achieved higher accuracy and efficiency for the majority of tested colors as compared to the RGB to HSV. *********************************************************** Membezakan antara warna adalah masalah yang merunsingkan terutamanya kepada mereka yang buta, separa buta atau buta warna. Tujuan kertas penyelidikan ini adalah untuk membentangkan kaedah menukar warna kepada bunyi dan getaran bagi membolehkan individu yang buta, separa buta atau buta warna untuk mendapat ‘perasaan’ atau pemahaman yang lebih baik tentang warna-warna yang berbeza disekeliling mereka. Idea yang dicadangkan adalah dengan membuat sebuah alat yang dapat menghasilkan getaran bagi setiap warna yang berbeza. Disamping itu, pengguna juga dapat mendengar nama warna tersebut. Algoritma yang digunakan untuk membezakan antara warna adalah penukaran warna RGB kepada HSV yang dibandingkan dengan rangkaian neural dan algoritma pembelajaran mesin berasaskan pokok keputusan. Raspberry Pi 3 bersaiz kad kredit dengan perisian Open Source Computer Vision (OpenCV) mengendalikan pemprosesan imej. Hasil algoritma penukaran warna RGB kepada HSV telah dilakukan dengan tiga warna yang berbeza (merah, biru, dan hijau). Tambahan pula, hasil rangkaian neural dan algoritma berasaskan pokok keputusan telah dilakukan dengan lapan warna (merah, hijau, biru, oren, kuning, ungu, putih, dan hitam) dengan penukaran warna tersebut kepada bunyi dan getaran. Selain itu, hasil rangkaian neural dan algoritma berasaskan pokok keputusan mencapai hasil dapatan yang baik dengan ketepatan dan kecekapan yang tinggi bagi kebanyakan warna yang diuji berbanding RGB kepada HSV.

Item Type:	Article (Journal)
Additional Information:	7157/57806
Uncontrolled Keywords:	Colors, Neural network, Image processing, Vibration, Sound.
Subjects:	T Technology > TJ Mechanical engineering and machinery
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Nik Nur Wahidah Nik Hashim
Date Deposited:	04 Dec 2018 12:44
Last Modified:	12 Jul 2019 10:04
URI:	http://irep.iium.edu.my/id/eprint/57806

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