Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and Cooperation
| Autor: | Carlos Norberto Fischer, Fabricio Breve |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Neural and Evolutionary Computing (cs.NE) business.industry Retraining Computer Science - Neural and Evolutionary Computing Path (graph theory) Task analysis Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer |
| Zdroj: | IJCNN |
| DOI: | 10.48550/arxiv.2005.04473 |
| Popis: | Navigation and mobility are some of the major problems faced by visually impaired people in their daily lives. Advances in computer vision led to the proposal of some navigation systems. However, most of them require expensive and/or heavy hardware. In this paper we propose the use of convolutional neural networks (CNN), transfer learning, and semi-supervised learning (SSL) to build a framework aimed at the visually impaired aid. It has low computational costs and, therefore, may be implemented on current smartphones, without relying on any additional equipment. The smartphone camera can be used to automatically take pictures of the path ahead. Then, they will be immediately classified, providing almost instantaneous feedback to the user. We also propose a dataset to train the classifiers, including indoor and outdoor situations with different types of light, floor, and obstacles. Many different CNN architectures are evaluated as feature extractors and classifiers, by fine-tuning weights pre-trained on a much larger dataset. The graph-based SSL method, known as particle competition and cooperation, is also used for classification, allowing feedback from the user to be incorporated without retraining the underlying network. 92\% and 80\% classification accuracy is achieved in the proposed dataset in the best supervised and SSL scenarios, respectively. Comment: BREVE, Fabricio Aparecido; FISCHER, Carlos Norberto. Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and Cooperation In: 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020, Glasgow, UK. Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020. (accepted for publication) |
| Databáze: | OpenAIRE |
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