Abstract:
A real-time object detection mobile application for visually impaired
individuals using TensorFlow Lite aims to detect objects and Indonesian currency
while providing language selection and other supporting features. Deep learning
models for object detection, particularly Convolutional Neural Networks, have been
an active field in computer vision research for many years. However, developing
object detection applications for mobile devices with limited computational power
might be challenging. The solution presented in this report utilizes TensorFlow Lite,
a lightweight variant of TensorFlow created for mobile and embedded devices. The
application uses EfficientDet, a highly efficient object detection model that enables
scaling of network width, depth, and resolution in a balanced manner, resulting in
improved accuracy and efficiency. The implementation of the application using
TensorFlow Lite and EfficientDet has resulted in a highly precise and reliable object
detection model suitable for mobile devices. The application also includes additional
features to improve usability, such as Indonesian currency detection. The future work
for this application includes improving the Indonesian currency detection feature,
expanding language options, and training custom models for specific objects or
features. This application has the potential to significantly improve the daily
experiences of visually impaired people and enhance their independence.
