This thesis describes new methods for automatic crack detection in pavements. Cracks in pavements can be used as an early indication for the need of reparation. Automatic crack detection is preferable compared to manual inventory; the repeatability can be better, the inventory can be done at a higher speed and can be done without interruption of the traffic. The automatic and semi-automatic crack detection systems that exist today use Image Analysis methods. There are today powerful methods available in the area of Computer Vision. These methods work in higher dimensions with greater complexity and generate measures of local signal properties, while Image Analyses methods for crack detection use morphological operations on binary images. Methods for digitalizing video data on VHS-cassettes and stitching images from nearby frames have been developed. Four methods for crack detection have been evaluated, and two of them have been used to form a crack detection and classification program implemented in the calculation program Matlab. One image set was used during the implementation and another image set was used for validation. The crack detection system did perform correct detection on 99.2 percent when analysing the images which were used during implementation. The result of the crack detection on the validation data was not very good. When the program is being used on data from other pavements than the one used during implementation, information about the surface texture is required to calibrate the crack detection.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-2818 |
| Date | January 2005 |
| Creators | Håkansson, Staffan |
| Publisher | Linköpings universitet, Bildbehandling, Linköpings universitet, Tekniska högskolan, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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