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VIDAR-Based Road-Surface-Pothole-Detection Method.
Xu, Yi; Sun, Teng; Ding, Shaohong; Yu, Jinxin; Kong, Xiangcun; Ni, Juan; Shi, Shuyue.
Afiliación
  • Xu Y; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
  • Sun T; Collaborative Innovation Center of New Energy Automotive, Shandong University of Technology, Zibo 255000, China.
  • Ding S; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
  • Yu J; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
  • Kong X; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
  • Ni J; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
  • Shi S; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel) ; 23(17)2023 Aug 28.
Article en En | MEDLINE | ID: mdl-37687924
This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By comparing it with the classical method and using the confusion matrix to judge the correctness, recall and accuracy of the method proposed in this paper, it is verified that the method proposed in this paper can improve the accuracy of monocular vision in detecting potholes in road surfaces.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza