Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently before processing. State-of-the-art pavement image processing methods executed on a CPU are not able to analyse pavement images in real time. To compensate this limitation of the methods, we propose an automated approach for pavement distress detection. In particular, GPU implementations of a noise removal, a background correction and a pavement distress detection method were developed. The median filter and the top-hat transform are used to remove noise and shadows in the images. The wavelet transform is applied in order to calculate a descriptor value for classification purposes. The approach was tested on 1549 images. The results show that real-time pre-processing and analysis are possible
