Developing Real – Time Ethiopian Currency Recognition Model for Visually Disabled Peoples Using Convolution
Domaine:
healthcareType de record:
paper
Créateur:
Mek
Éditeur:
Hus
Éditeur:
Nat
Hôte:
According to a survey report made by the Ethiopian Ministry of Health along with
several non-governmental organizations in the year 2006 G.C, there were about 5.3%
of the Ethiopian population lives with blindness and low vision problems. This
research work aims to develop a Convolutional Neural Network-based model by
using pre-trained models to enable blind and vision-impaired persons to recognize
Ethiopian currency banknotes in real-time scenarios. CNN addressed the aims to
recognize the currency banknote. The models attempt to accurately recognize
Ethiopian currency banknotes even if the input images come up with partially or
highly distorted and folded Birr notes.
Even if 60,000 Ethiopian currency banknote data are collected within real-life
situations by using nine blind persons the model was trained by only 8500 banknotes
(1700 data for each class) because of time constraints. The models were evaluated
with manually created datasets containing 500 real-time videos of different
conditions. The whole training, classification, and detection tasks have been modeled
by adopting Tensorflow Object Detection API and the pre-trained Faster R-CNN
Inception, and SSD MobileNet pre-trained models. The transfer learning technique is
applied by using the pre-trained models.
All the codes are implemented using Python. The model tested using numerous
Ethiopian currencies at different banknotes status and light conditions. In the case of
Faster R-CNN Inception model an average accuracy, precision, recall, and F1-score of
91.8%, 91.8%, 92.8%, and 91.8% are obtained respectively and in the case of SSD
MobileNet model an average accuracy, precision, recall, and F1-score of 79.4%,
79.4%, 93.6%, and 84.4% are obtained respectively within a real-time video.
Therefore as the first research work, the model has shown good performance in both
models but Faster R-CNN provides a promising result with an average accuracy of
91.8%
several non-governmental organizations in the year 2006 G.C, there were about 5.3%
of the Ethiopian population lives with blindness and low vision problems. This
research work aims to develop a Convolutional Neural Network-based model by
using pre-trained models to enable blind and vision-impaired persons to recognize
Ethiopian currency banknotes in real-time scenarios. CNN addressed the aims to
recognize the currency banknote. The models attempt to accurately recognize
Ethiopian currency banknotes even if the input images come up with partially or
highly distorted and folded Birr notes.
Even if 60,000 Ethiopian currency banknote data are collected within real-life
situations by using nine blind persons the model was trained by only 8500 banknotes
(1700 data for each class) because of time constraints. The models were evaluated
with manually created datasets containing 500 real-time videos of different
conditions. The whole training, classification, and detection tasks have been modeled
by adopting Tensorflow Object Detection API and the pre-trained Faster R-CNN
Inception, and SSD MobileNet pre-trained models. The transfer learning technique is
applied by using the pre-trained models.
All the codes are implemented using Python. The model tested using numerous
Ethiopian currencies at different banknotes status and light conditions. In the case of
Faster R-CNN Inception model an average accuracy, precision, recall, and F1-score of
91.8%, 91.8%, 92.8%, and 91.8% are obtained respectively and in the case of SSD
MobileNet model an average accuracy, precision, recall, and F1-score of 79.4%,
79.4%, 93.6%, and 84.4% are obtained respectively within a real-time video.
Therefore as the first research work, the model has shown good performance in both
models but Faster R-CNN provides a promising result with an average accuracy of
91.8%