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

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.