Ortho-image Creation for Obstructed Road Surfaces
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Solution Overview
Problem
Conventional methods for surveying road conditions, such as crack detection, are hindered by obstacles like trees and traffic lights, which prevent unmanned aerial vehicles (UAVs) from capturing complete ortho-images of road surfaces, making it impossible to survey cracks and road edges accurately.
Innovation Solution
The method involves using two UAVs, one flying higher and one lower than the obstacle, to capture images from different altitudes, acquiring three-dimensional coordinates of feature points, and creating corrected ortho-images by removing obstructed areas, allowing for comprehensive road surface analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an unmanned aerial vehicle photographs the road surface from the sky, then the road condition survey can be conducted efficiently, but obstacles such as trees and traffic lights block the view and prevent complete coverage of the road surface
Solution Approach 1:
The survey process is divided into two segments: aerial photography for uncovered areas and ground-level photography for obstacle-covered areas. This segmentation allows each method to be applied where it is most effective, maintaining high productivity while ensuring complete information coverage.
Solution Approach 2:
The solution transitions from a single aerial dimension to a multi-dimensional approach by combining aerial views with ground-level views. This dimensional change enables photography of areas blocked from above by obstacles, achieving complete road surface coverage without sacrificing survey efficiency.
2Measurement precision
If a road surface condition survey vehicle is used to detect cracks, then crack detection can be performed, but the vehicle cannot travel on narrow roads and the survey process becomes complicated
Solution Approach 1:
The mechanical road surface condition survey vehicle is replaced with an unmanned aerial vehicle equipped with photography and image processing capabilities. This substitution eliminates the operational constraints of narrow roads and complex survey procedures while maintaining crack detection accuracy through automated image analysis.
3Ease of operation
If only aerial photography is used, then the survey process is simplified, but areas covered by obstacles cannot be photographed or surveyed
Solution Approach 1:
The survey process is segmented into aerial photography for accessible areas and ground-level photography for obstacle-covered areas. This segmentation maintains operational simplicity by using automated processes while ensuring survey reliability through comprehensive coverage of all road surfaces.
Solution Approach 2:
The obstacle-covered areas serve as an intermediary zone that requires a different photography approach. By introducing ground-level photography as a mediator for these specific areas, the system maintains overall process simplicity while achieving complete and reliable survey coverage.
Data Source
AI summary
An ortho-image creation method includes: first photographing of photographing a road; second photographing of photographing an area covered by the obstacle from an altitude lower than the obstacle by a second photographing apparatus, and obtaining a plurality of second photographed images; first coordinate acquisition of acquiring three-dimensional coordinates; second coordinate acquisition of acquiring three-dimensional coordinates of a second feature point located in the area covered by the obstacle and included in at least two of the plurality of second photographed images; and ortho-image creation of creating a corrected ortho-image obtained by correcting at least a part of the area covered by the obstacle in the road surface to an area not covered by the obstacle, on the basis of the plurality of first photographed images, the plurality of second photographed images, the three-dimensional coordinates of the first feature point, and the three-dimensional coordinates of the second feature point.