In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The feasibility of the system is tested with assets along an 8 km section of the A27 highway on the south coast of England, UK. Survey data from a vehicle equipped with a single forward-facing camera and a GPS-enabled inertial measurement unit, aerial imagery of the highway, and the asset inventory are fused to develop the system. The camera on the vehicle is calibrated so that assets may be automatically located within the survey images. The assets are then classified by a state-of-the-art convolutional neural network. Therefore, those assets recorded correctly in the inventory and those needing further manual inspection are automatically identified. Three different asset types are considered (traffic signs, matrix signs, and reference marker posts), and overall 91% of the assets in a withheld test set are verified automatically. Thus the analyst is presented with a much smaller set of assets for which the inventory is incorrect and which require further inspection. We therefore demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring.

本文提出了一种决策支持系统，以协助分析人员更新高速公路路边资产清单。该系统的可行性已在英国英格兰南海岸的A27高速公路8公里处进行了资产测试。来自配备了单个前置摄像头和具有GPS功能的惯性测量单元的车辆的测量数据，高速公路的航拍图像以及资产清单被融合在一起，以开发该系统。车辆上的摄像机经过校准，因此资产可以自动位于勘测图像中。然后，通过最新的卷积神经网络对资产进行分类。因此，将自动识别正确记录在库存中的资产和需要进一步手动检查的资产。考虑了三种不同的资产类型（交通标志，矩阵标志，和参考标记信息），并且自动验证了预存测试集中91％的资产。因此，为分析人员提供的资产要少得多，它们的库存不正确，需要进一步检查。因此，我们证明了融合多个数据源以开发用于运输资产监控的决策支持系统的价值。