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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Rapid traffic sign damage inspection in natural scenes using mobile laser scanning data
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Rapid traffic sign damage inspection in natural scenes using mobile laser scanning data

机译：使用流动激光扫描数据在自然场景中快速交通标志损坏检查

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摘要

This paper proposes a novel approach for traffic sign detection and rapid damage inspection in natural scenes based on mobile laser scanning (MLS) data, including images and point clouds. The inspection results assist traffic management departments to take immediate measures to update and maintain traffic signs after natural disasters leading to many damaged traffic signs. Our approach involves four steps: Firstly, we use a deep learning network, Fast regions with convolutional neural network (Fast R-CNN), to train a traffic sign detector in an open benchmark, where the images are more variable and have a higher resolution. Then, traffic signs in images are detected by using the trained detector. Next, the area of the traffic sign, based on the sign area in the image, is roughly detected in MLS point clouds. Then, an accurate traffic sign is detected. Finally, some placement parameters of the traffic sign are measured for damage inspection and further inventory. Our proposed approach is validated on a set of point-clouds acquired by a RIEGL VMX-450 MLS system. Experimental results demonstrate that the rapidity and reliability of our proposed approach in traffic sign detection and damage inspection are robust.
机译：本文提出了一种基于移动激光扫描（MLS）数据的自然场景中交通标志检测和快速损坏检查的新方法，包括图像和点云。检查结果协助交通管理部门立即采取措施更新和维护自然灾害后导致许多受损交通标志的交通标志。我们的方法涉及四个步骤：首先，我们使用深度学习网络，具有卷积神经网络（FAST R-CNN）的快速区域，在开放基准中培训交通标志检测器，其中图像更具变量并具有更高的分辨率。然后，使用训练检测器检测图像中的交通标志。接下来，基于图像中的符号区域的交通标志区域大致检测到MLS点云中。然后，检测到准确的交通标志。最后，测量交通标志的一些放置参数，用于损坏检查和进一步的库存。我们所提出的方法在由RieGL VMX-450 MLS系统获取的一组点云上验证。实验结果表明，我们建议的交通标志检测和损害检查中的拟议方法的快速性和可靠性是强大的。

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