Memorias de investigación
Artículos en revistas:
A Simplified Computer Vision System for Road Surface Inspection and Maintenance
Año:2016
Áreas de investigación
  • Ingenierías,
  • Procesado y análisis de la señal,
  • Visión en tiempo-real
Datos
Descripción
This paper presents a computer vision system whose aim is to detect and classify cracks on road surfaces. Most of the previous works consisted of complex and expensive acquisition systems, whereas we have developed a simpler one composed by a single camera mounted on a light truck and no additional illumination. The system also includes tracking devices in order to geolocalize the captured images. The computer vision algorithm has three steps: hard shoulder detection, cell candidate proposal, and crack classification. First the region of interest (ROI) is delimited using the Hough transform (HT) to detect the hard shoulders. The cell candidate step is divided into two substeps: Hough transform features (HTF) and local binary pattern (LBP). Both of them split up the image into nonoverlapping small grid cells and also extract edge orientation and texture features, respectively. At the fusion stage, the detection is completed by mixing those techniques and obtaining the crack seeds. Afterward, their shape is improved using a new developed morphology operator. Finally, one classification based on the orientation of the detected lines has been applied following the Chain code. Massive experiments were performed on several stretches on a Spanish road showing very good performance.
Internacional
Si
JCR del ISI
Si
Título de la revista
Ieee Transactions on Intelligent Transportation Systems
ISSN
1524-9050
Factor de impacto JCR
2,534
Información de impacto
Datos JCR del año 2015
Volumen
17
DOI
10.1109/TITS.2015.2482222
Número de revista
3
Desde la página
608
Hasta la página
619
Mes
MARZO
Ranking
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Aplicación de Telecomunicaciones Visuales (G@TV)