PHOTOGRAMMETRIC MODELLING OF STREET CONDITIONS USING  A VEHICLE-MOUNTED NON-METRIC CAMERA

ALENA FUAD  TOMA; RAAD AWAD  KATTAN

doi:10.26682/sjuod.2022.25.2.36

ALENA FUAD TOMA Dept. of Civil Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
RAAD AWAD KATTAN Dept. of Surveying Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/sjuod.2022.25.2.36

Keywords: Non-metric camera, Close-range photogrammetry, Ground control points, Distresses, Focal length, Convergent Photos

Abstract

Monitoring and modeling pavement distresses can employ either simple methods like direct measurements by tapes or straight edges, or more sophisticated approaches like laser scanners and stereoscopic cameras held at the ends of fixed poles.

This study aims to detect pavement distress’s shape and position, such as cracks. For this purpose, overlapped images were collected for the distressed area to obtain complete coverage of the street conditions located in front of the College of Engineering /University of Duhok. Kurdistan Region, Iraq. A single non-metric camera attached to the side and the front of the vehicle is the approach employed as an image-collecting device. The camera shutter speed is selected to have a sequence of overlapped images to be processed, producing the pavement surface orthomosaic.

The plane of the image in this study is inclined or obliquely relative to the pavement surface, so a gap is expected in the overlap area in the near range. To overcome this drawback and other problems, an indoor grid and tiles test was conducted to obtain the required parameters such as focal length, overlap percentage, camera shutter speed, and vehicle speed.

Ground control points (GCPs) and checkpoints for processing and accuracy checks were provided in each test and distributed evenly in the model area.

The indoor parameter collecting tests revealed that the Root Mean Square Error (RMSE) for the (15) checkpoints was ±0.013m in X and ±0.016m in Y coordinates. Using a larger tilt angle and increasing camera height gave a root mean square error of ±0.009m in X and ±0.013m in Y, respectively.

Image processing was conducted using Agisoft PhotoScan as it provided adequate accuracy and modeling tools in several previous papers

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