Improving Visual Road Condition Assessment by Extensive Experiments on the Extended GAPs Dataset

Aging public roads need frequent inspections in order to guarantee their permanent availability. In many countries, this includes the standardized visual assessment of millions of images. Due to the lack of sophisticated approaches, often, the evaluation is done manually and therefore requires excessive manual labor. GAPs is the most extensive publicly available dataset that provides standardized, high-quality images for training deep neural networks for pavement distress detection. We further enlarge this dataset and provide refined annotations. By conducting extensive experiments on the GAPs dataset, we improve the performance of automated visual road condition assessment. We evaluate the performance gain of several modern neural network architectures and advanced training techniques.