Novel Methodology to Recover Road Surface Height Maps from Illuminated Scene through Convolutional Neural Networks

Road surface properties have a major impact on pavement’s life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilometers are widely used, even though they have two...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (17), p.6603
Main Authors: de León, Gonzalo, Cesbron, Julien, Klein, Philippe, Leandri, Pietro, Losa, Massimo
Format: Article
Language:English
Subjects:
Analysis
Artificial intelligence
Cameras
convolutional neural networks
Datasets
deep learning
Digital cameras
Engineering Sciences
Lasers
Light
Light sources
Low cost
Methodology
Methods
Neural networks
Photogrammetry
photometric stereo
profilometer
Profilometers
Road surface
Roads & highways
Sensors
Spatial data
Surface properties
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Summary:Road surface properties have a major impact on pavement’s life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilometers are widely used, even though they have two major drawbacks: spatial information is missed and the cost of the equipment is considerable. The scope of this work is to show the methodology used to develop a fast and low-cost system using images taken with a commercial camera to recover the height information of the road surface using Convolutional Neural Networks. Hence, the dataset was created ad hoc. Based on photometric theory, a closed black-box with four light sources positioned around the surface sample was built. The surface was provided with markers in order to link the ground truth measurements carried out with a laser profilometer and their corresponding intensity values. The proposed network was trained, validated and tested on the created dataset. Three loss functions where studied. The results showed the Binary Cross Entropy loss to be the most performing and the best overall on the reconstruction task. The methodology described in this study shows the feasibility of a low-cost system using commercial cameras based on Artificial Intelligence.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22176603