The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 209–217, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-209-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 209–217, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-209-2017

  27 Sep 2017

27 Sep 2017

OBJECT-BASED AND SUPERVISED DETECTION OF POTHOLES AND CRACKS FROM THE PAVEMENT IMAGES ACQUIRED BY UAV

Y. Pan1, X. Zhang1,2, M. Sun1,2, and Q. Zhao2 Y. Pan et al.
  • 1Institute of Remote Sensing and Geographic Information System, Peking University, 5 Summer Palace Road, Beijing 100871, China
  • 2School of Computer Science, Shihezi Univeristy, Shihezi, Xinjiang 832002, China

Keywords: Pothole, Crack, UAV, Remote sensing, Supervised learning, Pavement distress

Abstract. Roads are the basic element of land transportation system. After construction, the quality of road will decrease because of the aging and deterioration of the road surface. In the end, some distresses will appear on the pavement, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, nowadays some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, LiDAR and Radar. In our study, the digital pavement images acquired by Unmanned Aerial Vehicle (UAV) and four popular supervised learning algorithms (KNN, SVM, ANN, RF) were used to distinguish between the normal pavement and pavement damages (i.e. cracks and potholes). Each of learning algorithms was given a series of different parameters, and the classification accuracy and computational time as two assessment criteria of the algorithm performance were calculated. Finally, four best models for each kind of learning algorithms were selected based on the standard of highest accuracy and minimum running time.