  Road rutting is a severe road distress that can cause premature failure of
road incurring early and costly maintenance costs. Research on road damage
detection using image processing techniques and deep learning are being
actively conducted in the past few years. However, these researches are mostly
focused on detection of cracks, potholes, and their variants. Very few research
has been done on the detection of road rutting. This paper proposes a novel
road rutting dataset comprising of 949 images and provides both object level
and pixel level annotations. Object detection models and semantic segmentation
models were deployed to detect road rutting on the proposed dataset, and
quantitative and qualitative analysis of model predictions were done to
evaluate model performance and identify challenges faced in the detection of
road rutting using the proposed method. Object detection model YOLOX-s achieves
<EMAIL_ADDRESS>of 61.6% and semantic segmentation model PSPNet (Resnet-50)
achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark
accuracy for similar work in future. The proposed road rutting dataset and the
results of our research study will help accelerate the research on detection of
road rutting using deep learning.
