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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Deep learning-based traffic sign recognition for unmanned autonomous vehicles
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Deep learning-based traffic sign recognition for unmanned autonomous vehicles

机译：基于深度学习的无人驾驶汽车交通标志识别

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摘要

Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network-based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network-based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.
机译：作为无人驾驶自动驾驶汽车的关键技术之一，交通标志识别被应用于辅助自动驾驶。颜色是识别交通标志的重要线索。但是，基于颜色的方法在光线变化的情况下会降低性能。卷积神经网络作为深度学习方法之一，能够从原始输入中分层学习高级特征。已经证明，基于卷积神经网络的方法优于基于颜色的方法。目前，卷积神经网络的输入被处理为灰度图像或三个独立的颜色通道。所学习的颜色特征仍然不足以表示交通标志。除颜色外，时间限制对于识别基于视频的交通标志也至关重要。时域交通标志的特性需要进一步探索。四元数能够编码多维信息，并且已被用于描述彩色图像。在本文中，我们受到启发，提出了一种基于四元数卷积神经网络的方法，通过在单个框架中融合时空特征来识别交通标志。实验结果表明，与最新技术相比，该方法可以产生正确的识别结果，并获得更好的性能。

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