Engineering and Applied Science
These research positions focuses on the research and development of a system able to process images of the surface of flexible road pavements, to allow for the automatic detection and characterization of road surface defects. Although cracks with linear development are considered the most common pavement surface degradation found by road inspectors, other important surface defects like cracks with alligator pattern, potholes, routing, raveling, among others, represent important data about pavement surface condition to allow for an adequate quantitative and qualitative evaluation of road pavement surface degradation. Images of road surface taken using imaging systems composed of one regular camera (considered as been non-expensive imaging systems) have been used by researchers, mainly on the detection of cracks with linear development. However, these images are 2D data structures, been difficult to detect defects like potholes, routing or raveling when processing them, because the depth information along a profile of the road defect is missing. Light-field cameras, capturing light ray directions, have recently become available. The processing of the richer light field images allows a more complete analysis of the road pavement surface, for instance supporting depth estimation. An alternative/complementary sensor that can be used for road surface defect detection is a mobile Light Detection and Ranging (LiDAR). From this type of sensor a 3D point cloud, representing the transversal profiles of a road lane, can be created for the desired analysis. This project exploits the usage of these two types of imaging sensors. More information can be found at: https://www.it.pt/Positions/OtherResearchPosition/5082
-MSc on Computer Science and Engineering, or similar qualifications; -Strong preference is given to candidates with research experience on computer vision, image processing and pattern recognition; -Significant academic and practical background in programming languages/environments: C/C++, OpenCV, Python and preferably also Matlab; -Very good knowledge of spoken and written English; -Strong self-motivation for scientific research – possibility to join a PhD program in Electrical and Computer Engineering; -Ability for teamwork;
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|Number of Awards||2|
|Includes||Two research positions are open: 1) CrackIT-LF: (i) exploring the capabilities of light-field cameras to create a 3D data structure of the road pavement surface; (ii) develop feature extraction techniques directly exploiting the richer light field data representation; (iii) develop classification techniques to identify the relevant road degradations, including longitudinal, transversal and alligator pattern cracks; potholes; rutting and raveling; (iv) develop a friendly graphical user interface to the developed analysis tools. 2) CrackIT-LiDAR: (i) exploring the capabilities of point clouds acquired by a mobile LiDAR system to allow the creation of a road pavement surface 3D data structure; (ii) detection and characterization of pavement surface distresses along with their vertical deformations based on the intensity information and the 3D point structure; (iii) develop a friendly graphical user interface to the developed analysis tools.|