  Maintaining roads is crucial to economic growth and citizen well-being
because roads are a vital means of transportation. In various countries, the
inspection of road surfaces is still done manually, however, to automate it,
research interest is now focused on detecting the road surface defects via the
visual data. While, previous research has been focused on deep learning methods
which tend to process the entire image and leads to heavy computational cost.
In this study, we focus our attention on improving the classification
performance while keeping the computational cost of our solution low. Instead
of processing the whole image, we introduce a segmentation model to only focus
the downstream classification model to the road surface in the image.
Furthermore, we employ contrastive learning during model training to improve
the road surface condition classification. Our experiments on the public RTK
dataset demonstrate a significant improvement in our proposed method when
compared to previous works.
