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Rapid traffic sign damage inspection in natural scenes using mobile laser scanning data

机译：使用移动激光扫描数据在自然场景中快速进行交通标志损坏检查

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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（包括图像和点云）的自然场景中交通标志检测和快速损伤检查的新方法。检查结果有助于交通管理部门在自然灾害导致许多交通标志损坏后立即采取措施更新和维护交通标志。我们的方法涉及四个步骤：首先，我们使用深度学习网络，带卷积神经网络的快速区域（Fast R-CNN），在开放基准测试中训练交通标志检测器，在该基准中图像变化更大且分辨率更高。然后，使用训练有素的检测器检测图像中的交通标志。接下来，基于图像中的标志区域，在MLS点云中大致检测到交通标志的区域。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以进行损坏检查和进一步清点。我们提出的方法在RIEGL VMX-450 MLS系统获取的一组点云上得到了验证。实验结果表明，我们提出的方法在交通标志检测和损坏检查中的快速性和可靠性很强。

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