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Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs...

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Detalles Bibliográficos
Autores principales: Nartey, Obed Tettey, Yang, Guowu, Asare, Sarpong Kwadwo, Wu, Jinzhao, Frempong, Lady Nadia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248915/
https://www.ncbi.nlm.nih.gov/pubmed/32397197
http://dx.doi.org/10.3390/s20092684
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author Nartey, Obed Tettey
Yang, Guowu
Asare, Sarpong Kwadwo
Wu, Jinzhao
Frempong, Lady Nadia
author_facet Nartey, Obed Tettey
Yang, Guowu
Asare, Sarpong Kwadwo
Wu, Jinzhao
Frempong, Lady Nadia
author_sort Nartey, Obed Tettey
collection PubMed
description Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications.
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spelling pubmed-72489152020-06-10 Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning Nartey, Obed Tettey Yang, Guowu Asare, Sarpong Kwadwo Wu, Jinzhao Frempong, Lady Nadia Sensors (Basel) Article Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications. MDPI 2020-05-08 /pmc/articles/PMC7248915/ /pubmed/32397197 http://dx.doi.org/10.3390/s20092684 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nartey, Obed Tettey
Yang, Guowu
Asare, Sarpong Kwadwo
Wu, Jinzhao
Frempong, Lady Nadia
Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title_full Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title_fullStr Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title_full_unstemmed Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title_short Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning
title_sort robust semi-supervised traffic sign recognition via self-training and weakly-supervised learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248915/
https://www.ncbi.nlm.nih.gov/pubmed/32397197
http://dx.doi.org/10.3390/s20092684
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