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首页> 外文期刊>Neural processing letters >A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets
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A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets

机译：交通标志识别新型遗传优化的卷积神经网络：比利时和中国交通标志数据集的新基准

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摘要

Traffic signs are a key constituent of the road network and prove to be very useful for warning and guiding the drivers. In intelligent transport systems, traffic sign recognition (TSR) is indispensable for autonomous driving. However, due to the complex outdoor environment, real-time recognition of traffic signs is much more challenging in comparison with many other pattern recognition tasks. Convolutional neural networks (CNNs) have an exceptional capability of recognizing patterns and are one of the most popular deep learning techniques. Finding the optimal configuration of a CNN for a task is a major challenge and is an active area of research. Genetic algorithm (GA) is a meta-heuristic approach well-known for its optimization power. In this paper, we propose a novel deep learning technique based on the concept of domain transfer learning for the recognition of traffic signs. This technique utilizes a newly proposed variant of the GA for finding the optimal values of the number of epochs and the learning rate parameter for each layer of the pre-trained CNN model (VGG-16). To examine the effectiveness of our technique, we apply it to the following two benchmark datasets of TSR: Belgium Traffic Sign Classification (BTSC) dataset and Chinese Traffic Sign Dataset (TT100K). The results indicate that our model outperforms all the existing approaches applied to these datasets and gives a new benchmark of the recognition accuracies of 99.16% for the BTSC and 96.28% for the TT100K datasets, thus establishing the robustness of our model.
机译：交通标志是道路网络的关键组成部分，并证明是非常有用的警告和指导司机。在智能运输系统中，交通标志识别（TSR）对于自主驾驶是必不可少的。然而，由于户外环境复杂，与许多其他模式识别任务相比，实时识别交通标志的实际识别要具有挑战性。卷积神经网络（CNNS）具有识别模式的特殊能力，是最受欢迎的深度学习技术之一。找到任务的CNN的最佳配置是一个主要的挑战，是一个有效的研究领域。遗传算法（GA）是一种符合其优化功率的元启发式方法。在本文中，我们提出了一种基于域传输学习概念来识别交通标志的新型深度学习技术。该技术利用GA的新提出的变型来查找每层训练的CNN模型（VGG-16）的每层时代的数量和学习率参数的最佳值。要检查我们技术的有效性，我们将其应用于TSR的以下两个基准数据集：比利时交通标志分类（BTSC）DataSet和中国交通标志数据集（TT100K）。结果表明，我们的模型优于应用于这些数据集的所有现有方法，并为BTSC提供了99.16％的识别精度的新基准，而TT100K数据集的96.28％，因此建立了模型的稳健性。

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