Roadway asset inventory data are essential in making data-driven asset management decisions. Despite significant advances in automated data processing, the current state of the practice is semi-automated. This paper demonstrates integration of the state-of-the-art artificial intelligence technologies within a practical framework for automated real-time identification of traffic signs from roadway images. The framework deploys one of the very latest machine learning algorithms on a cutting-edge plug-and-play device for superior effectiveness, efficiency, and reliability. The proposed platform provides an offline system onboard the survey vehicle, that runs a lightweight and speedy deep neural network on each collected roadway image and identifies traffic signs in real-time. Integration of these advanced technologies minimizes the need for subjective and time-consuming human interventions, thereby enhancing the repeatability and cost-effectiveness of the asset inventory process. The proposed framework is demonstrated using a real-world image dataset. Appropriate pre-processing techniques were employed to alleviate limitations in the training dataset. A deep learning algorithm was trained for detection, classification, and localization of traffic signs from roadway imagery. The success metrics based on this demonstration indicate that the algorithm was effective in identifying traffic signs with high accuracy on a test dataset that was not used for model development. Additionally, the algorithm exhibited this high accuracy consistently among the different considered sign categories. Moreover, the algorithm was repeatable among multiple runs and reproducible across different locations. Above all, the real-time processing capability of the proposed solution reduces the time between data collection and delivery, which enhances the data-driven decision-making process.

巷道资产清单数据对于制定数据驱动的资产管理决策至关重要。尽管在自动化数据处理方面取得了重大进步，但该实践的当前状态是半自动化的。本文演示了将最新的人工智能技术集成到一个实用的框架中，该框架可从道路图像中自动实时识别交通标志。该框架在尖端的即插即用设备上部署了最新的机器学习算法之一，以实现卓越的有效性，效率和可靠性。所提出的平台提供了一种在勘测车上的离线系统，该系统在每个采集的道路图像上运行轻量级且快速的深度神经网络，并实时识别交通标志。这些先进技术的集成最大程度地减少了对主观且耗时的人工干预的需求，从而提高了资产清单过程的可重复性和成本效益。使用真实世界的图像数据集演示了所提出的框架。采用适当的预处理技术来减轻训练数据集中的限制。训练了深度学习算法，用于检测，分类和定位来自道路图像的交通标志。基于此演示的成功指标表明，该算法可以有效地识别未用于模型开发的测试数据集上的交通标志。另外，该算法在不同的考虑符号类别之间始终展现出这种高精度。此外，该算法在多次运行之间可重复，并且在不同位置均可重现。最重要的是，所提出的解决方案的实时处理能力减少了数据收集和交付之间的时间，从而增强了数据驱动的决策过程。