Detecting and mapping traffic signs from Google Street View images using deep learning and GIS
Detecting and mapping traffic signs from Google Street View images using deep learning and GIS
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DOI：
10.1016/j.compenvurbsys.2019.101350
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发表时间：
2019-09-01
影响因子：
6.8
通讯作者：
Sun, Qian (Chayn)
中科院分区：
文献类型：
--
作者：
Campbell, Andrew;Both, Alan;Sun, Qian (Chayn)
道路交通标志基础设施由于其多样化的物理结构和地理分布，仍然是地方政府管理的一项极其困难的资产。交通基础设施空间登记员目前是地方政府委员会强制性道路管理计划的必要组成部分。机器学习中对象检测技术的最新进展为Google街景（GSV）图像捕获的街道标牌的检测和分类提供了一种自动化方法。本文探讨了使用深度学习来生成一个自主系统的可能性，该系统用于检测GSV图像上的交通标志，以协助交通资产的监控和维护。通过利用谷歌的街景API，这项研究提供了一种经济的方法来构建有目的的路标计算机视觉数据集。训练自定义对象检测模型，以从在交叉口方法捕获的图像中检测和分类停止和让路标志。考虑到输出检测到的包围盒坐标，应用摄影测量方法计算每个检测到的标志在二维地理空间中的近似位置。新定位和分类的街道标志可以与相关空间数据相结合，以实现到资产管理系统中。通过结合GIS和GSV API，该过程完全可扩展到任何级别的路标分类范围。在研究区域的道路网络上进行的实验记录的检测准确率为95.63%，分类准确率为97.82%。我们提出的自动化方法来检测和定位的路标基础设施已显示出其使用的地方政府当局的一个有前途的潜力。我们的工作流程可以用于检测其他交通标志，并应用于其他路段和其他城市。最重要的是，这种方法始终采用完全免费和开源的方法。谷歌街景计划的延续将为这一宝贵资产的持续维护和更新计划提供街道标志基础设施的时空表示。
Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset.