Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

中文翻译：

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基于轻量级网络和多层提议网络的快速交通标志检测方法

基于视觉的交通标志检测在智能交通系统中起着至关重要的作用。最近，已经提出了许多基于深度学习的交通标志检测方法，与传统方法相比，它们表现出更好的性能。然而，由于驾驶环境中的困难条件以及交通场景图像中交通标志的大小，基于深度学习的方法在小交通标志检测方面的性能仍然受到限制。另外，当前最先进的交通标志检测方法的推理速度仍然很慢。本文提出了一种基于深度学习的方法，以提高驾驶环境中小交通标志检测的性能。首先，采用轻便高效的架构作为基础网络，以解决推理速度问题。为了提高小交通标志检测的性能，采用了去卷积模块，通过将低层特征图与高层特征图进行聚合，生成增强型特征图。然后，使用两个改进的区域提议网络从最高级别的特征图和增强的特征图生成提议。所提出的改进的区域提议网络被设计用于快速和准确的提议生成。在实验中，德国交通标志检测基准数据集用于评估每个增强模块的有效性，而清华腾讯100K数据集用于将提议的方法与其他最新方法的有效性进行比较。交通标志检测。