| dc.description.abstract | Pavement condition assessment is an important process in road maintenance and monitoring. However, current pavement condition assessment methods are time consuming and usually implemented manually. Pavement distress is becoming one of the biggest problems in road network systems. An efficient and effective detection method is needed to identify these pavement problems on the road. Additionally, crack detection is much more complicated than other infrastructure element detection. Current crack detection methods are complex and inefficient. Therefore, the purpose of this study is to create an efficient and effective crack detection model to identify cracks based on pavement images. This study uses a crack detection model with four components: image collection, image segmentation, pixel selection and statistical analysis. Image collection is a process of using a photogrammetry method to collect the data for the crack detection model. Then, the pavement image is transformed into a binary image during the image segmentation phase. Subsequently, the pixels in the binary image are selected in order to convert the photogrammetry data into statistical data. Finally, the statistical data will be analyzed using linear regression in order to determine whether the linear regression line represents an actual center line for the crack. This crack model is performed in a MATLAB prototype. The results indicate that this crack detection model can identify the center line of a crack accurately. Future work is suggested on controlling the data is piece of the linear regression algorithm. | en |
