Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

中文翻译：

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用于大规模交通标志检测和识别的深度学习

交通标志的自动检测和识别在交通标志库存管理中起着至关重要的作用。它提供了一种准确、及时的方法，以最少的人力来管理交通标志库存。在计算机视觉社区中，交通标志的识别和检测是一个经过充分研究的问题。绝大多数现有方法在高级驾驶员辅助和自主系统所需的交通标志上表现良好。然而，这代表所有交通标志中相对较少的数量（数百个类别中的大约 50 个类别），其余交通标志集的性能仍然是一个悬而未决的问题. 在本文中，我们解决了检测和识别大量适合自动化交通标志库存管理的交通标志类别的问题。我们采用卷积神经网络 (CNN) 方法，即掩码 R-CNN，通过自动端到端学习来解决检测和识别的完整流程。我们提出了几项改进措施，这些改进措施在交通标志检测方面进行了评估，从而提高了整体性能。这种方法适用于检测我们新数据集中表示的 200 个交通标志类别。结果报告在以前工作中尚未考虑的极具挑战性的交通标志类别上。