The legibility of traffic signs has been considered from the beginning of design, and traffic signs are easy to identify for humans. For computer systems, however, identifying traffic signs still poses a challenging problem. Both image-processing and machine-learning algorithms are constantly improving, aimed at better solving this problem. However, with a dramatic increase in the number of traffic signs, labelling a large amount of training data means high cost. Therefore, how to use a small number of labelled traffic sign data reasonably to build an efficient and high-quality traffic sign recognition (TSR) model in the Internet-of-things–based (IOT-based) transport system has been an urgent research goal. Here, the authors propose a novel semi-supervised learning approach combining global and local features for TSR in an IOT-based transport system. In their approach, histograms of oriented gradient, colour histograms (CH), and edge features (EF) are used to build different feature spaces. Meanwhile, on the unlabelled samples, a fusion feature space is found to alleviate the differences between different feature spaces. Extensive evaluations on a collection of signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed approach outperforms the others and provides a potential solution for practical applications.

中文翻译：

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基于半监督分类的全局和局部特征相结合的交通标志识别

从设计之初就已经考虑了交通标志的可读性，并且交通标志很容易被人识别。然而，对于计算机系统而言，识别交通标志仍然是一个具有挑战性的问题。图像处理和机器学习算法都在不断改进，以更好地解决这个问题。然而，随着交通标志数量的急剧增加，标记大量的训练数据意味着高成本。因此，如何在物联网（IOT）传输系统中合理使用少量带标签的交通标志数据来构建高效，高质量的交通标志识别（TSR）模型已成为当务之急。目标。这里，作者提出了一种新颖的半监督学习方法，该方法结合了基于物联网的运输系统中TSR的全局和局部特征。在他们的方法中，定向梯度的直方图，颜色直方图（CH）和边缘特征（EF）用于构建不同的特征空间。同时，在未标记样本上，发现融合特征空间可以缓解不同特征空间之间的差异。对来自德国交通标志识别基准（GTSRB）数据集的标志进行的广泛评估表明，该方法优于其他方法，并为实际应用提供了潜在的解决方案。发现融合特征空间可以减轻不同特征空间之间的差异。对来自德国交通标志识别基准（GTSRB）数据集的标志进行的广泛评估表明，该方法优于其他方法，并为实际应用提供了潜在的解决方案。发现融合特征空间可以减轻不同特征空间之间的差异。对来自德国交通标志识别基准（GTSRB）数据集的标志进行的广泛评估表明，该方法优于其他方法，并为实际应用提供了潜在的解决方案。