Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale-variance and geometric variations. To address these problems, this study proposes a region-based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi-branch sample pyramid module is proposed, which is based on multi-branch CNNs for multi-scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale-aware multi-task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end-to-end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.

中文翻译：

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用于交通标志检测和分类的尺度感知有限可变形卷积神经网络

交通标志的检测和分类是智能交通系统的重要组成部分，可用于通知自动无人驾驶系统和驾驶辅助系统有关道路的状况和限制。尽管计算机视觉已广泛用于交通标志检测中，但是由于标志类型，比例变化和几何变化的多样性，在全球范围内检测和识别交通标志仍然是一个巨大的挑战。为了解决这些问题，本研究提出了一种基于区域的深度卷积神经网络（CNN）框架，用于交通标志检测和分类。具体而言，提出了一种基于多分支CNN的多分支样本金字塔模块，用于多尺度特征提取。然后将有限的可变形卷积模块嵌入到CNN层中，以了解变形信息表示以进行变形处理。此外，规模感知的多任务区域提议网络模块被应用于检测各种规模的交通标志。整个网络以端到端的方式训练。最后，对两个公共检测数据集进行了实验，以证明该方法的有效性。