Innovation Owner
Miss VARNAVORN LIMBOONSUEBSAI
Student
Details
This thesis presents the application of deep learning for object classification using CNN and ResNet18 architectures. The goal is to develop an efficient model for assistive devices that help visually impaired individuals identify indoor objects and receive sound alerts.
This thesis presents the application of deep learning for object classification. The selected deep learning architectures studied include:
- Convolutional Neural Networks (CNN)
- ResNet18
It covers data preparation, feature extraction, parameter tuning for accuracy comparison, and performance evaluation of the selected models. The aim is to propose an efficient model for use in devices that assist visually impaired individuals in classifying indoor objects and providing sound alerts.
Objective
The objectives include studying image processing and deep learning algorithms to design and build an assistive device that identifies obstacles for visually impaired individuals navigating indoors.
- Study image processing.
- Study deep learning algorithms for object classification.
- Design and build a device to assist visually impaired individuals in classifying objects that act as obstacles to movement inside buildings.