ABSTRACT

The PICture Unsupervised Classification with Human Analysis (PICUCHA) refers to a hybrid human-artificial intelligence methodology for pavement distresses assessment. It combines the human flexibility to recognize patterns and features in images with the neural network ability to expand such recognition to large volumes of images. In this study, the PICUCHA performance was tested with images taken with area-scan cameras and flash light illumination over a pavement with dark textures. These images are particularly challenging for the analysis because of the lens distortion and non-homogeneous illumination, generating artificial joints that happened at random positions inside the image cells. The chosen images were previously analyzed by other software without success because of the dark coluor. The PICUCHA algorithms could analyze the images with no noticeable problem and without any image pre-processing, such as contrast or brightness adjustments. Because of the special procedure used by the pavement engineer for the key patterns description, the distresses detection accuracy of the PICUCHA for the particular image set could reach 100%.